An information theoretic perspective of the sparse coding

Hideitsu Hino, Noboru Murata

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

The sparse coding method is formulated as an information theoretic optimization problem. The rate distortion theory leads to an objective functional which can be interpreted as an information theoretic formulation of the sparse coding. Viewing as an entropy minimization problem, the rate distortion theory and consequently the sparse coding are extended to discriminative variants. As a concrete example of this information theoretic sparse coding, a discriminative non-linear sparse coding algorithm with neural networks is proposed. Experimental results of gender classification by face images show that the discriminative sparse coding is more robust to noise, compared to the conventional method which directly uses images as inputs to a linear support vector machine.

本文言語English
ホスト出版物のタイトルAdvances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings
ページ84-93
ページ数10
PART 1
DOI
出版ステータスPublished - 2009 9 11
イベント6th International Symposium on Neural Networks, ISNN 2009 - Wuhan, China
継続期間: 2009 5 262009 5 29

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
5551 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference6th International Symposium on Neural Networks, ISNN 2009
CountryChina
CityWuhan
Period09/5/2609/5/29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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