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Correlation Mining for Imaging and Multidimensional Signal Processing

Alfred HeroProfessor University of Michigan, Department of Electrical Engineering & Computer Science
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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.

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University of Michigan, Department of Electrical Engineering & Computer Science