Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models

Irina Rish, Genady Grabarnik, Guillermo A. Cecchi, Francisco Pereira, Geoffrey J. Gordon

ICML 2008 pp. 832-839

doi:10.1145/1390156.1390261 /icml/2008/rish2008icml-closed/

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.

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Text

Rish et al. "Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390261

Markdown

[Rish et al. "Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/rish2008icml-closed/) doi:10.1145/1390156.1390261

BibTeX

@inproceedings{rish2008icml-closed,
  title     = {{Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models}},
  author    = {Rish, Irina and Grabarnik, Genady and Cecchi, Guillermo A. and Pereira, Francisco and Gordon, Geoffrey J.},
  booktitle = {International Conference on Machine Learning},
  year      = {2008},
  pages     = {832-839},
  doi       = {10.1145/1390156.1390261},
  url       = {https://mlanthology.org/icml/2008/rish2008icml-closed/}
}