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Publication
ICML 2008
Conference paper
Closed-form supervised dimensionality reduction with generalized linear models
Abstract
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. Copyright 2008 by the author(s)/owner(s).