Sliced inverse regression with conditional entropy minimization

Hideitsu Hino, Keigo Wakayama, Noboru Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages1185-1188
Number of pages4
Publication statusPublished - 2012 Dec 1
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 2012 Nov 112012 Nov 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

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

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Hino, H., Wakayama, K., & Murata, N. (2012). Sliced inverse regression with conditional entropy minimization. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 1185-1188). [6460349] (Proceedings - International Conference on Pattern Recognition).