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Activity Number: 698
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #316883 View Presentation
Title: Task-Driven Dimension Reduction in the Presence of Nuisance Variables
Author(s): David Shaw* and Aswin Sankanarayanan and Rama Chellappa
Companies: and Carnegie Mellon University and University of Maryland
Keywords: sufficient dimension reduction ; linear mixed model ; discriminant analysis ; semiparametric model ; covariate shift ; computer vision

High-dimensional problems abound in applied statistics. Practitioners often require methods to reduce the dimension of a given data set while retaining the salient information contained therein in order to aid decision-making processes such as assessing variable importance, providing visualization, or performing variable selection. Dimension reduction methods seek a projection of data into a lower-dimensional subspace that preserves or exploits some desired underlying structure. Unfortunately, a number of dimension reduction techniques assume a single source of data, and many further assume identically distributed data, perhaps conditioned on some variable of interest. We propose a framework to both incorporate knowledge of response variables of interest into dimension reduction techniques as well as take into account nuisance variables that affect the homogeneity of the data distribution. The framework extends to estimation of sparse projections, allowing for variable selection in addition to removing heterogeneity. We apply our methods to a variety of high-dimensional data analysis problems in image analysis.

Authors who are presenting talks have a * after their name.

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