Sliced inverse regression with conditional entropy minimization

Hideitsu Hino, Keigo Wakayama, Noboru Murata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

An appropriate dimension reduction of raw data helps to reduce computational time and to reveal the intrinsic structure of complex data. In this paper, a dimension reduction method for regression is proposed. The method is based on the well-known sliced inverse regression and conditional entropy minimization. Using entropy as a measure of dispersion of data distribution, dimension reduction subspace is estimated without assuming regression function form nor data distribution, unlike conventional sliced inverse regression. The proposed method is shown to perform well compared to some conventional methods through experiments using both artificial and real-world data sets.

本文言語English
ホスト出版物のタイトルICPR 2012 - 21st International Conference on Pattern Recognition
ページ1185-1188
ページ数4
出版ステータスPublished - 2012 12 1
イベント21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
継続期間: 2012 11 112012 11 15

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period12/11/1112/11/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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