Closed-form supervised dimensionality reduction with generalized linear models

Authors:
Irina Rish;Genady Grabarnik;Guillermo Cecchi;Francisco Pereira;Geoffrey J. Gordon
Affiliations:
IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;Princeton University, Princeton, NJ;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the 25th international conference on Machine learning
Year:
2008

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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.