Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models (Technical Report (2008)
| Venue: | IBM T.J. Watson Research Center |
| Citations: | 8 - 1 self |
BibTeX
@ARTICLE{Rish08closed-formsupervised,
author = {Irina Rish and Genady Grabarnik and Guillermo Cecchi and Francisco Pereira and Geoffrey J. Gordon},
title = {Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models (Technical Report},
journal = {IBM T.J. Watson Research Center},
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. 1.
Citations
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| 5 | Efficient bounds for the softmax and applications to approximate inference in hybrid models - Bouchard - 2007 |
| 1 | Alon Orlitsky (2005). Supervised dimensionality reduction using mixture models - Sajama |
