Correlation Mining for Imaging and Multidimensional Signal Processing

Abstract
Correlation mining is a class of methods for extracting complex patterns from massive multivariate datasets, such as spatio-temporal data and images. Many operations on such datasets depend on computing a large number of correlations, including linear prediction, texture discrimination, and Gauss Markov random-field modeling. In this talk, I will present emerging mehods of correlation mining for massive datasets, discuss some of the underlying mathematical theory, and illustrate with several applications.