Pattern Change Discovery Between High Dimensional Datasets

This technology provides a powerful solution to the general problem of pattern change discovery between high-dimensional data sets. Current technologies either mainly focus on magnitude change detection of low-dimensional data sets or are under supervised frameworks. In this technology, the notion of the principal angles between the subspaces is introduced to measure the subspace difference between two high-dimensional data sets. Principal angles bear a property to isolate subspace change from the magnitude change. To address the challenge of directly computing the principal angles, we elect to use matrix factorization to serve as a statistical framework and develop the principle of the dominant subspace mapping to transfer the principal angle-based detection to a matrix factorization problem. We show how matrix factorization can be naturally embedded into the likelihood ratio test based on the linear models. The developed technology is of an unsupervised nature and addresses the statistical significance of the pattern changes between high-dimensional data sets. We have showcased the different applications of this technology in several specific real-world applications to demonstrate the power and effectiveness of this technology.


For high dimensional data, the subspace change (pattern change) reveals information more significant than the magnitude change does. This technology innovatively isolates the subspace change from the magnitude change while all the existing Euclidean metric based technologies mix the two types of changes together and thus are unable to discover the pattern change effectively.


Binghamton University RB405

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