RI: Small: Spectral Methods for Learning Time Series and Graphical Models

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

The investigators study a new class of statistical methods for learning time series and graphical models. Their approach is based on spectral analysis and matrix decomposition methods that have enjoyed tremendous success in applications, but their use in graphical models has drawn less attention. The goal of this investigation is to extend the enormous previous successes of matrix decomposition methods to the realm of more complicated time series and certain graphical models, which will lead to new statistical machine learning algorithms with important practical applications. In the information age, an important measure of computer intelligence is the ability to analyze huge amount of data that become available electronically, and make critical decisions under uncertain environment. Statistical machine learning is the main technique for analyzing electronic data, and graphical models are mathematical tools for understanding these complex data both by computer systems and by human operators in order to facilitate decision making. However, traditional algorithms for learning graphical models have limitations that restrict capabilities of modern computing systems. The current research attempts a new class of mathematical algorithms that can be used to design more effective graphical models, which in turn allows modern computers to analyze data more accurately and achieve higher level of intelligence.