Regularized discriminant entropy analysis

Haitao Zhao; Wai Keung Wong

doi:10.1016/j.patcog.2013.08.020

Regularized discriminant entropy analysis

Haitao Zhao, Wai Keung Wong

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

11 Citations (Scopus)

Abstract

In this paper, we propose the regularized discriminant entropy (RDE) which considers both class information and scatter information on original data. Based on the results of maximizing the RDE, we develop a supervised feature extraction algorithm called regularized discriminant entropy analysis (RDEA). RDEA is quite simple and requires no approximation in theoretical derivation. The experiments with several publicly available data sets show the feasibility and effectiveness of the proposed algorithm with encouraging results.

Original language	English
Pages (from-to)	806-819
Number of pages	14
Journal	Pattern Recognition
Volume	47
Issue number	2
DOIs	https://doi.org/10.1016/j.patcog.2013.08.020
Publication status	Published - 1 Feb 2014

Keywords

Discriminant entropy analysis
Entropy-based learning
Regularized discriminant entropy

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

More information

10.1016/j.patcog.2013.08.020

Cite this

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KW - Discriminant entropy analysis

KW - Entropy-based learning

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Regularized discriminant entropy analysis

Abstract

Keywords

ASJC Scopus subject areas

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