Cognitive Analytics Answers the Question: What’s Interesting in Your Data?
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 and when telling your data’s story.
There is a “good news, bad news” angle here. First, the bad news: the human capacity for visualizing 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.
Machine learning algorithms (e.g., the random forest algorithm) are increasingly effective at finding the most explanatory (most predictive) features in big data. But that presumes that you already know what needs to be explained! That is a supervised learning approach (in which you know in advance the key classes of objects and events represented within your data). But what if you don’t those key classes yet? How do you find the interesting features within your data in the first place? That requires an unsupervised learning approach along with some human understanding of what defines “interesting.”
Consequently, a cognitive analytics approach that combines the best of both worlds (machine learning 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.
Computer Vision (CV) is a methodology (based on a set of algorithms) that enables computers to interpret what a sensor visually perceives. CV is not a new field, but it has traditionally been applied primarily to image processing and image analysis. CV algorithms include edge-detection, gradient-detection, motion-detection, change-detection, object-detection, segmentation, template-matching, and pattern recognition. Many of these same algorithms can be applied to high-dimensional data streams that are not images but are “scenes” (such as still frames in a grand tour) that are projections of high-dimensionality data into lower-dimension parameter spaces. This is truly a cognitive analytics approach.
One possible outcome of using CV is the generation of “interestingness metrics” that signal to the data end-user the most interesting and informative features (or combinations of features) in high-dimensional data (or that are discovered in a grand tour). Interestingness can be measured using specific observable parameters or can be inferred via the detection of interesting patterns in the data. An example of the latter is latent (hidden) variable discovery.
Latent variables are not explicitly observed but are inferred from the observed features in a data set. Latent variables are inferred primarily because they are the variables that cause the all-important interesting descriptive, predictive, and explanatory patterns seen in the data set. Latent variables can also be concepts that are implicitly represented by the data (e.g.
