The Exponentially Embedded Family of Distributions for Effective Data Representation, Information Extraction, and Decision Making

Abstract

Abstract : We have focused on the mathematical formalism and stochastic machine learning algorithms for extraction of relevant information based on the exponentially embedded family (EEF), learning and classification over large-scale stream data, and information fusion and integration. In particular, we have proposed a probability density function (PDF) estimation approach based on the EEF, and a measure for assessment of information from sensors. We have also taken advantage of the model structure information for model estimation. Furthermore, we have proved a general Pythagorean theorem for the EEF and studied a multi path scenario for sensor selection. Finally, we also analyzed and developed a series of machine learning techniques for effective data learning, classification, and decision making, including adaptive incremental learning from stream data, information fusion with multiple learning models/hypotheses, machine learning with non-stationary imbalanced stream data, kernel density estimation based on self-organizing map(SOM), among others. These results have been published in peer-reviewed conferences and journals, including IEEE Transactions on Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing (Elsevier), a book chapter with Wiley-IEEE, among others.