Category Archives: Machine Learning

Meta-Learning For Better Machine Learning

Leave a reply

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. An example of a cold start problem is k-Means Clustering, where the number of clusters k in the data set is not known in advance, and the locations of those clusters in feature space (i.e., the cluster means) are not known either. So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. See the related post for more details about the cold start challenge. See the attached graphic below for a simple demonstration of a k-Means Clustering application.

The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data). There are also examples of cold start in supervised machine learning (where you do have class labels on the training data).

As an example of a cold start in supervised learning, we look at neural network models, where the weights on the edges that connect the various nodes in the network layers are not known initially. So, random values (e.g., all weights = 1) are assigned to all of the edge weights (which could number in the hundreds or thousands) — that’s the cold start. Following that, the weights can “learn” to get better through a technique known as backpropagation, which is applied through sequential iterations of the neural network learning process. A validation metric estimates the error in each model iteration in the sequence (i.e., the classification error on the validation or hold-out data set), then applies the backpropagation technique to assign some portion of the error to each of the edge weights. Each edge weight is adjusted accordingly using gradient descent (or some similar error correction rate estimator) for the next model in the sequence. The next iteration of the neural network modeling process is executed, applying the same steps as above, and the process continues until the validation metric converges to the optimal final model.

What is missing in the above discussion is the deeper set of unknowns in the learning process. This is the meta-learning phase. We can elucidate this phase through our two examples above.

From the first example above, k-Means Clustering:

  • What is the value of k?
  • Which features in the data set are most effective in creating distinct clusters in the data (i.e., to create the segments that are the most compact internally, and relatively the most separated from each other)? There might be dozens or hundreds or thousands of attributes to choose from, and a vast number of combinations of those attributes in which to explore clustering in different dimensions of parameter space.
  • What distance metric should be used to estimate separation (or what similarity metric should be used to estimate similarity), since clustering is a distance-based algorithm? There are some common choices for distance and similarity metrics (e.g., cosine similarity, Euclidean distance, Manhattan distance, Mahalanobis distance, Lp-Norm, etc.), but that is just the tip of a vast iceberg — just take a look at the 750-page book “Encyclopedia of Distances“.
  • What evaluation metric should we use to determine if the clusters are “good enough” or optimal (i.e., the most compact set of clusters relative to the separation of the clusters)? There are several choices for such evaluation metrics: Dunn index, Davies-Bouldin index, C-index, and Silhouette analysis are just a few examples.

We need to decide on all of these parameterizations of the clustering model before the cold start interations on the cluster means can begin.

From the second example above, Neural Network modeling, there are also many different preliminary tasks and parameterizations of the network that need to be decided and acted on before the cold start iterations on the edge weights can begin:

This now gets to the heart of meta-learning. It is focused on learning the right tasks to perform and on tuning the modeling hyper-parameters. These are the different tasks and “external” parameters that differentiate various instantiations of a specific model within a broader category of models — those tasks and external parameterizations must be explored before you start building, iterating, and validating a specific model’s “internal” parameters. For example:

  • You can cluster children’s toys in a toy store by color, or by shape, or by electronic vs. non-electronic, or by age-appropriateness, or by functionality, or cluster them by some combination of those features.
  • You can cluster (segment) your customers by the types of products they buy, or by their geographic location, or by their gender, or by their age, or by the day of week that they prefer to shop, or cluster them by some combination of those many different variables.
  • You can cluster medical drug treatments by the types of symptoms that they address, or by the medical diagnoses (outcomes) that they attempt to cure, or by their dosage amounts, or cluster them by the side-effects that are caused when different combinations of the drugs are used.

Deciding on the higher-level hyper-parameterizations of your clustering approach before you build the actual models is good data science and good business, no matter whether you are sorting toys, or discovering segments in your customer database, or prescribing different medications to medical patients.

Similar decisions must be made for the neural network example mentioned earlier as well as for numerous other machine learning modeling techniques. Meta-learning is important to make sure that you are aware of and attentive to the many choices of modeling tasks and parameterizations for the models that you are about to train. Meta-learning is also critical for demonstrating (proving) that you built the best (or optimal or most accurate) model, given the higher level characteristics (e.g., parameters, architecture, or input data sources) of the modeling effort:

  • What is the business case? What outcomes will be actionable?
  • What data do we have? Which combinations of data have we not explored yet?
  • What metric will demonstrate that we have achieved the globally optimal model (or approximately the global optimum), versus some locally good model that doesn’t generalize across a larger data set?

Genetic Algorithms (GAs) are an example of meta-learning. They are not machine learning algorithms in themselves, but GAs can be applied across ensembles of machine learning models and tasks, in order to find the optimal model (perhaps globally optimal model) across a collection of locally optimal solutions.

Learn more about meta-learning from these resources:

Finally, in addition to the awesome 750-page book “Encyclopedia of Distances“, please check out some of these top-selling books on Data Science, AI, and Machine Learning:

Top Books in AI and Machine Learning

Top Books in AI and Machine Learning

Disclosure statement: As an Amazon Associate I earn from qualifying purchases.

Data Scientist’s Dilemma – The Cold Start Problem

Leave a reply

The ancient philosopher Confucius has been credited with saying “study your past to know your future.” This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning.

Without labels (diagnoses, classes, known outcomes) in past data, then how do we make progress in labeling (explaining) future data? This would be a problem.

A related problem also arises in unsupervised machine learning. In these applications, there is no requirement or presumption regarding the existence of labeled training data — we are essentially parameterizing or characterizing the patterns in the data (e.g., the trends, correlations, segments, clusters, associations).

Many unsupervised learning models can converge more readily and be more valuable if we know in advance which parameterizations are best to choose. If we cannot know that (i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data.

In both of these applications (supervised and unsupervised machine learning), if we don’t have these initial insights and validation metrics, then how does such model-building get started and get moving towards the optimal solution?

This challenge is known as the cold-start problem! The solution to the problem is easy (sort of): We make a guess — an initial guess! Usually, that would be a totally random guess.

That sounds so… so… random! How do we know whether it’s a good initial guess? How do we progress our model (parameterizations) from that random initial choice? How do we know that our progression is moving towards more accurate models? How? How? How?

This can be a real challenge. Of course nobody said the “cold start” problem would be easy. Anyone who has ever tried to start a very cold car on a frozen morning knows the pain of a cold start challenge. Nothing can be more frustrating on such a morning. But, nothing can be more exhilarating and uplifting on such a morning than that moment when the engine starts and the car begins moving forward with increasing performance.

The experiences for data scientists who face cold-start problems in machine learning can be very similar to those, especially the excitement when our models begin moving forward with increasing performance.

We will itemize several examples at the end. But before we do that, let’s address the objective function. That is the true key that unlocks performance in a cold-start challenge.  That’s the magic ingredient in most of the examples that we will list.

The objective function (also known as cost function, or benefit function) provides an objective measure of model performance. It might be as simple as the percentage of class labels that the model got right (in a classification model), or the sum of the squares of the deviations of the points from the model curve (in a regression model), or the compactness of the clusters relative to their separation (in a clustering analysis).

The value of the objective function is not only in its final value (i.e., giving us a quantitative overall model performance rating), but its great (perhaps greatest) value is realized in guiding our progression from the initial random model (cold-start zero point) to that final successful (hopefully, optimal) model. In those intermediate steps it serves as an evaluation (or validation) metric.

By measuring the evaluation metric at step zero (cold-start), then measuring it again after making adjustments to the model parameters, we learn whether our adjustments led to a better performing model or worse performance. We then know whether to continue making model parameter adjustments in the same direction or in the opposite direction. This is called gradient descent.

Gradient descent methods basically find the slope (i.e., the gradient) of the performance error curve as we progress from one model to the next. As we learned in grade school algebra class, we need two points to find the slope of a curve. Therefore, it is only after we have run and evaluated two models that we will have two performance points — the slope of the curve at the latest point then informs our next choice of model parameter adjustments: either (a) keep adjusting in the same direction as the previous step (if the performance error decreased) to continue descending the error curve; or (b) adjust in the opposite direction (if the performance error increased) to turn around and start descending the error curve.

Note that hill-climbing is the opposite of gradient descent, but essentially the same thing. Instead of minimizing error (a cost function), hill-climbing focuses on maximizing accuracy (a benefit function). Again, we measure the slope of the performance curve from two models, then proceed in the direction of better-performing models. In both cases (hill-climbing and gradient descent), we hope to reach an optimal point (maximum accuracy or minimum error), and then declare that to be the best solution. And that is amazing and satisfying when we remember that we started (as a cold-start) with an initial random guess at the solution.

When our machine learning model has many parameters (which could be thousands for a deep neural network), the calculations are more complex (perhaps involving a multi-dimensional gradient calculation, known as a tensor). But the principle is the same: quantitatively discover at each step in the model-building progression which adjustments (size and direction) are needed in each one of the model parameters in order to progress towards the optimal value of the objective function (e.g., minimize errors, maximize accuracy, maximize goodness of fit, maximize precision, minimize false positives, etc.). In deep learning, as in typical neural network models, the method by which those adjustments to the model parameters are estimated (i.e., for each of the edge weights between the network nodes) is called backpropagation. That is still based on gradient descent.

One way to think about gradient descent, backpropagation, and perhaps all machine learning is this: “Machine Learning is the set of mathematical algorithms that learn from experience. Good judgment comes experience. And experience comes from bad judgment.” In our case, the initial guess for our random cold-start model can be considered “bad judgment”, but then experience (i.e., the feedback from validation metrics such as gradient descent) bring “good judgment” (better models) into our model-building workflow.

Here are ten examples of cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution:

  • Clustering analysis (such as K-Means Clustering), where the initial cluster means and the number of clusters are not known in advance (and thus are chosen randomly initially), but the compactness of the clusters can be used to evaluate, iterate, and improve the set of clusters in a progression to the final optimum set of clusters (i.e., the most compact and best separated clusters).
  • Neural networks, where the initial weights on the network edges are assigned randomly (a cold-start), but backpropagation is used to iterate the model to the optimal network (with highest classification performance).
  • TensorFlow deep learning, which uses the same backpropagation technique of simpler neural networks, but the calculation of the weight adjustments is made across a very high-dimensional parameter space of deep network layers and edge weights using tensors.
  • Regression, which uses the sum of the squares of the deviations of the points from the model curve in order to find the best-fit curve. In linear regression, there is a closed-form solution (derivable from the linear least-squares technique). The solution for non-linear regression is not typically a closed-form set of mathematical equations, but the minimization of the sum of the squares of deviations still applies — gradient descent can be used in an iterative workflow to find the optimal curve. Note that K-Means Clustering is actually an example of piecewise regression.
  • Nonconvex optimization, where the objective function has many hills and valleys, so that gradient descent and hill-climbing will typically converge only to a local optimum, not to the global optimum. Techniques like genetic algorithms, particle swarm optimization (when the gradient cannot be calculated), and other evolutionary computing methods are used to generate lots of random (cold-start) models and then iterate each of them until you find the global optimum (or until you run out of time and resources, and then pick the best one that you could find). [See my graphic attached below that illustrates a sample use case for genetic algorithms. See also the NOTE below the graphic about Genetic Algorithms, which also applies to other evolutionary algorithms, indicating that these are not machine learning algorithms specifically, but they are actually meta-learning algorithms]
  • kNN (k-Nearest Neighbors), which is a supervised learning technique in which the data set itself becomes the model. In other words, the assignment of a new data point to a particular group (which may or may not have a class label or a particular meaning yet) is based simply upon finding which category (group) of existing data points is in the majority when you take a vote of the nearest neighbors to the new data point. The number of nearest neighbors that are to be examined is some number k, which can be initially arbitrary (a cold-start), but then it is adjusted to improve model performance.
  • Naive Bayes classification, which applies Bayes theorem to a large data set with class labels on the data items, but for which some combinations of attributes and features are not represented in the training data (i.e., a cold-start challenge). By assuming that the different attributes are mutually independent features of the data items, then one can estimate the posterior likelihood for what the class label should be for a new data item with a feature vector (set of attributes) that is not found in the training data. This is sometimes called a Bayes Belief Network (BBN) and is another example of where the data set becomes the model, where the frequency of occurrence of the different attributes individually can inform the expected frequency of occurrence of different combinations of the attributes.
  • Markov modeling (Belief Networks for Sequences) is an extension of BBN to sequences, which can include web logs, purchase patterns, gene sequences, speech samples, videos, stock prices, or any other temporal or spatial or parametric sequence.
  • Association rule mining, which searches for co-occurring associations that occur higher than expected from a random sampling of a data set. Association rule mining is yet another example where the data set becomes the model, where no prior knowledge of the associations is known (i.e., a cold-start challenge). This technique is also called Market Basket Analysis, which has been used for simple cold-start customer purchase recommendations, but it also has been used in such exotic use cases as tropical storm (hurricane) intensification prediction.
  • Social network (link) analysis, where the patterns in the network (e.g., centrality, reach, degrees of separation, density, cliques, etc.) encode knowledge about the network (e.g., most authoritative or influential nodes in the network), through the application of algorithms like PageRank, without any prior knowledge about those patterns (i.e., a cold-start).

Finally, as a bonus, we mention a special case, Recommender Engines, where the cold-start problem is a subject of ongoing research. The research challenge is to find the optimal recommendation for a new customer or for a new product that has not been seen before. Check out these articles  related to this challenge:

  1. The Cold Start Problem for Recommender Systems
  2. Tackling the Cold Start Problem in Recommender Systems
  3. Approaching the Cold Start Problem in Recommender Systems

We started this article mentioning Confucius and his wisdom. Here is another form of wisdom: https://rapidminer.com/wisdom/ — the RapidMiner Wisdom conference. It is a wonderful conference, with many excellent tutorials, use cases, applications, and customer testimonials. I was honored to be the keynote speaker for their 2018 conference in New Orleans, where I spoke about “Clearing the Fog around Data Science and Machine Learning: The Usual Suspects in Some Unusual Places”. You can find my slide presentation here: KirkBorne-RMWisdom2018.pdf 

NOTE: Genetic Algorithms (GAs) are an example of meta-learning. They are not machine learning algorithms in themselves, but GAs can be applied across ensembles of machine learning models and tasks, in order to find the optimal model (perhaps globally optimal model) across a collection of locally optimal solutions.

Data Makes Possible Many Things: Insights Discovery, Innovation, and Better Decisions

Leave a reply

In the early days of the big data era (at the peak of the big data hype), we would often hear about the 3 V’s of big data (Volume, Variety, and Velocity). Then, people started adding more V’s, including Veracity and Value, plus many more! I was guilty of adding several more in my article “Top 10 Big Data Challenges – A Serious Look at 10 Big Data V’s“.

Through the years, I have decided on the following “4 V’s of Big Data” summary in my own presentations:

  • Volume = the most annoying V
  • Velocity = the most challenging V
  • Variety = the most rich V for insights discovery, innovation, and better decisions
  • Value = the most important V

As Dez Blanchfield once said, “You don’t need a data scientist to tell you big data is valuable. You need one to show its value.”

A series of articles that drive home the all-important value of data is being published on the DataMakesPossible.com site. The site’s domain name says it all: Data Makes Possible!

What does data make possible? I occasionally discuss these things in articles that I write for the MapR blog series, where I have summarized the big data value proposition very simply in my own list of the 3 D2D‘s of big data:

  • Data-to-Discovery (Class Discovery, Correlation Discovery, Novelty Discovery, Association Discovery)
  • Data-to-Decisions
  • Data-to-Dollars (or Data-to-Dividends)

The story is richer and much more impactful these days than those trivial 1-letter (V) or 3-letter (D2D) mnemonics would suggest. We are far past the peak of inflated expectations in the big data hype cycle, and we are even beyond the trough of disillusionment. We have truly entered the plateau of productivity from data in our organizations, though the analytics-driven culture is still going through growing pains.

So, what does data make possible? I document our progress toward deriving big value from big data in a series of articles that I am writing (with more to come) for the DataMakesPossible.com site. These articles include:

But don’t stop there! There are many more fabulous articles, insights, case studies, and impactful stories at DataMakesPossible.com — visit the site often, as there are new posts every week.

Let’s improve our world together through the insights and discoveries that large, comprehensive data collections can provide! That’s what data scientists love to do.

Follow Kirk Borne on Twitter @KirkDBorne

Recent top-selling books in AI and Machine Learning

Leave a reply
Top Books in AI and Machine Learning

Top Books in AI and Machine Learning

Below are the individual links to these Data Science, Artificial Intelligence and Machine Learning books, all of which are top sellers…

“The Hundred-Page Machine Learning Book”

“The Book of Why: The New Science of Cause and Effect”

“Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”

“Deep Learning (Adaptive Computation and Machine Learning)”

“Applied Artificial Intelligence: A Handbook For Business Leaders”

“Machine Learning For Absolute Beginners: A Plain English Introduction”

“Life 3.0: Being Human in the Age of Artificial Intelligence”

“An Introduction to Statistical Learning: with Applications in R” (7th printing; 2017 edition)

“Grokking Algorithms: An illustrated guide for programmers and other curious people”

“Prediction Machines: The Simple Economics of Artificial Intelligence”

“Deep Learning with Python”

“Python Machine Learning” (2nd edition)

“Advances in Financial Machine Learning”

“The Future of Leadership: Rise of Automation, Robotics and Artificial Intelligence”

“Deep Learning With Python: Step By Step Guide With Keras and Pytorch”

“Deep Reinforcement Learning Hands-On”

“Pattern Recognition and Machine Learning”

Disclosure statement: As an Amazon Associate I earn from qualifying purchases.

Machine Learning Making Big Moves in Marketing

Leave a reply

Machine Learning is (or should be) a core component of any marketing program now, especially in digital marketing campaigns. The following insightful quote by Dan Olley (EVP of Product Development and CTO at Elsevier) sums up the urgency and criticality of the situation: “If CIOs invested in machine learning three years ago, they would have wasted their money. But if they wait another three years, they will never catch up.” This statement also applies to CMOs.

To illustrate and to motivate these emerging and growing developments in marketing, we list here some of the top Machine Learning trends that we see:

  1. Hyper-personalization (SegOne context-driven marketing)
  2. Real-time sentiment analysis and response (social customer care)
  3. Behavioral analytics (predictive and prescriptive)
  4. Conversational chatbots (using NLG: Natural Language Generation)
  5. Agile analytics (DataOps)
  6. Influencer marketing (amplification of your message to specific audiences)
  7. Journey Sciences (using graph and linked data modeling)
  8. Context-based customer engagement through IoT (knowing the knowable via ubiquitous sensors)

You can read more details about each of these developments in my MapR blog.

And check out the excellent resources and services (in Data Analytics, Data Science, Machine Learning, AI, and Operations Research) at DataPrime Inc., to help all of your data-driven campaigns make big moves and move forward more effectively.

Four Ways to Harness Big Data in the Energy Sector

Leave a reply

The big change in every industry – energy included – is the sensoring of the world. We have put sensors on just about everything, and that’s definitely true in the energy sector, whether it’s in electricity, oil and gas, supply chain and manufacturing, or even customer interaction.

We call that big data, which people sometimes take to mean as big volume, but I like to think of as big value. Because with all this information, we can understand how our systems work – and put them to better use to create greater value for our business.

Prescriptive models

There is greater insight than ever before into how energy is distributed, both in the course of the day and across specific locations. In NASA, we tracked potential ‘killer asteroids’ – asteroids that have the potential to do serious damage to our earth.

In the energy industry that increased insight will help when averting its own disasters, or ‘killer asteroids’. Both predictive and prescriptive models are particularly useful here, for predicting future outcomes and for prescribing different (better, optimal) outcomes: if enough data are collected from factors such as the environment, devices used, and contextual information such as weather patterns or energy usage, models now know how to set the user parameters to prevent disaster.

On an energy grid, if a bad outcome is upcoming, conditions can be set to prevent machine failure, whether through reducing the operating temperature or speed, or increasing the frequency of on/off cycles.

Digital twins

An exciting proposition for the industrial sector is the digital twin, a full Computer-Aided Design (CAD) model of any device that can be run alongside its original to track behaviour and identify the cause of any failures. In the energy industry, this could be a digital replica of an offshore wind turbine, for example, monitoring power usage, production and efficiencies, and replaying any anomalies to identify their cause.

Crowdsourcing

When I was at NASA, we worked with scientists worldwide to create an online web portal called Zooniverse that presents large data collections to the public, enabling everyone to contribute to scientific discovery in datasets that are too large for the science teams to explore by themselves.

With the mountains of data we had it might take years, even centuries, to look through. But if you put it online and create an interesting enough challenge around that data, people across the world are going to volunteer their time in the name of scientific research.

We had some incredible results from this crowdsourcing mission. A Dutch schoolteacher called Hanny van Arkel discovered a strange green object while she was looking through images, which turned out to be an entirely new type of astronomical object (and is now named after her).

If I was to hypothesize how you could similarly crowdsource data in the energy sector, perhaps you could have people look at data prior to blackouts. There could be a pattern to the outage, or they could identify places where the power stays on during these blackouts – such as schools or grocery stores – which could be pinpointed on an app so people could use them when there are cuts.

Move beyond reporting

Energy industry executives should be interested in big data, because it allows them to look at all aspects of their business. I have conversations with publicly traded companies who only report on an annual or quarterly basis. I understand this is a regulatory requirement, but it seems to me to be only acting in hindsight.

You can’t drive a vehicle by looking in the rear-view mirror. You have to understand what’s coming, and the only way you can do that is by taking in all the information that is available to you: from the child playing on the street beside the road, to the truck two cars ahead.

Big data can deal with hindsight – events that have happened in the past; oversight – events happening now; and foresight – events happening in the future. And prescriptive modelling gives insight into all of these events. At a time where the global energy industry is undergoing huge change, don’t we need that forward-looking view of the rich information embedded within our big data reservoirs?

Summary

Therefore, the energy industry can benefit from these four approaches (prescriptive modelling, digital twinning, crowdsourcing, and moving beyond reporting) as ways to manage the digital disruption and the flood of new data coming from sensors everywhere. Industry leaders used to encourage organizations to adopt certain products or methodologies to help their data analytics move at the speed of business. I believe now that this is not a viable approach. What you really need are solutions that can help your business move at the speed of your data!

———————–

NOTE: This post was originally published at https://www.linkedin.com/pulse/four-ways-harness-big-data-energy-sector-kirk-borne/

Facilitate Proactive Cybersecurity Operations with Big Data Analytics and Machine Intelligence

Leave a reply

Digital processes are disrupting and transforming organizations everywhere. Big Data, Machine Learning, AI, and their applications companion, Machine Intelligence, are the soul of this digital revolution. These applications are probably no more essential anywhere else than in cybersecurity operations.

The quantity and complexity of digital network information has skyrocketed in recent years due to the explosion of internet-connected devices, the rise of operational technologies (OT), and the growth of an interconnected global economy (including IOT: the Internet of Things). With exponentially multiplying mountains of human- and machine-generated network data, the ability to extract meaningful signals about potentially nefarious activities, and ultimately deter those activities, has become increasingly complex.

In other domains, such as marketing and e-commerce, businesses have been able to effectively apply data mining to create customer “journeys” in order to predict and recommend content or products to the end-user. However, within cybersecurity operations, the ability to map the journey of an analyst or an adversary is inherently complex due to the dynamic nature of computer networks, the sophistication of adversaries, and the pervasiveness of technical and human factors that expose network vulnerabilities. Despite these challenges, there is hope for making meaningful progress. E-commerce marketing and cyber operations share one significant factor—the primary actor is a human being, whose interests, intents, motivations, and goals often manifest through their actions, behaviors, and other digital breadcrumbs.

For modern cybersecurity operations to be effective, it is necessary for organizations to monitor digital breadcrumbs from diverse data streams to identify strong activity signals. But, it doesn’t stop at monitoring sensors for signals. Our cybersecurity applications must proceed proactively from sensors (data collectors) to signals (big data) to sentinels (pattern detection and recognition, including the identification of early warnings and creation of alerts, through the algorithms of AI and machine learning) to sense-making (insights and action, through machine intelligence).

You can learn more about extracting meaningful signals from mountains of data and instrumenting advanced analytics for improved cyber defenses in the new report from Booz Allen Hamilton. Download and read your copy of the free report “Modernizing Cyber Security Operations with Machine Intelligence” here: https://www.oreilly.com/ideas/modernizing-cybersecurity-approaches

For additional insights into Booz Allen Hamilton’s Machine Intelligence capabilities, and how they can help your organization, download your copy of the “Machine Intelligence Primer” here: http://www.boozallen.com/machineintelligence

Discovering and understanding patterns in highly dimensional data

Leave a reply

Dimensionality reduction is a critical component of any solution dealing with massive data collections. Being able to sift through a mountain of data efficiently in order to find the key descriptive, predictive, and explanatory features of the collection is a fundamental required capability for coping with the Big Data avalanche. Identifying the most interesting dimensions of data is especially valuable when visualizing high-dimensional (high-variety) big data.

There is a “good news, bad news” angle here. First, the bad news: the human capacity for seeing multiple dimensions is very limited: 3 or 4 dimensions are manageable; 5 or 6 dimensions are possible; but more dimensions are difficult-to-impossible to assimilate. Now for the good news: the human cognitive ability to detect patterns, anomalies, changes, or other “features” in a large complex “scene” surpasses most computer algorithms for speed and effectiveness. In this case, a “scene” refers to any small-n projection of a larger-N parameter space of variables.

In data visualization, a systematic ordered parameter sweep through an ensemble of small-n projections (scenes) is often referred to as a “grand tour”, which allows a human viewer of the visualization sequence to see quickly any patterns or trends or anomalies in the large-N parameter space. Even such “grand tours” can miss salient (explanatory) features of the data, especially when the ratio N/n is large.

Consequently, a data analytics approach that combines the best of both worlds (machine algorithms and human perception) will enable efficient and effective exploration of large high-dimensional data. One such approach is to apply Computer Vision algorithms, which are designed to emulate human perception and cognitive abilities. Another approach is to generate “interestingness metrics” that signal to the data end-user the most interesting and informative features (or combinations of features) in high-dimensional data. A specific example of the latter is latent (hidden) variable discovery.

Latent variables are not explicitly observed but are inferred from the observed features, specifically because they are the variables that deliver the all-important (but sometimes hidden) descriptive, predictive, and explanatory power of the data set. Latent variables can also be concepts that are implicitly represented by the data (e.g., the “sentiment” of the author of a social media posting).  

Because some latent variables are “observable” in the sense that they can be generated through a “yet to be discovered” mathematical combination of several of the measured variables, these are therefore an obvious example of dimension reduction for visual exploration of large high-dimensional data.

Latent (Hidden) Variable Models are used in statistics to infer variables that are not observed but are inferred from the variables that are observed. Latent variables are widely used in social science, psychology, economics, life sciences and machine learning. In machine learning, many problems involve collection of high-dimensional multivariate observations and then hypothesizing a model that explains them. In such models, the role of the latent variables is to represent properties that have not been directly observed.

After inferring the existence of latent variables, the next challenge is to understand them. This can be achieved by exploring their relationship with the observed variables (e.g., using Bayesian methods) . Several correlation measures and dimensionality reduction methods such as PCA can be used to measure those relationships. Since we don’t know in advance what relationships exist between the latent variables and the observed variables, more generalized nonparametric measures like the Maximal Information Coefficient (MIC) can be used.

MIC has become popular recently, to some extent because it provides a straightforward R-squared type of estimate to measure dependency among variables in a high-dimensional data set.  Since we don’t know in advance what a latent variable actually represents, it is not possible to predict the type of relationship that it might possess with the observed variables. Consequently, a nonparametric approach makes sense in the case of large high-dimensional data, for which the interrelationships among the many variables is a mystery. Exploring variables that possess the largest values of MIC can help us to understand the type of relationships that the latent variables have with the existing variables, thereby achieving both dimension reduction and a parameter space in which to conduct visual exploration of high-dimensional data.

The techniques described here can help data end-users to discover and understand data patterns that may lead to interesting insights within their massive data collections.

Follow Kirk Borne on Twitter @KirkDBorne