Closed-form Supervised Dimensionality Reduction with Generalized Linear Models

author: Irina Rish, IBM Thomas J. Watson Research Center
published: Aug. 29, 2008,   recorded: July 2008,   views: 432
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Description

We propose a family of supervised dimensionality reduction (SDR) algorithms that combine feature extraction (dimensionality reduction) with learning a predictive model in a unified optimization framework, using data- and class-appropriate generalized linear models (GLMs), and handling both classification and regression problems. Our approach uses simple closed-form update rules and is provably convergent. Promising empirical results are demonstrated on a variety of high-dimensional datasets.

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Download slides icon Download slides: icml08_rish_cfsdr_01.pdf (2.8 MB)

Download slides icon Download slides: icml08_rish_cfsdr_01.ppt (5.9 MB)


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