Discriminant feature fusion strategy for supervised learning

Jun Bao Li; Shu Chuan Chu; Jung Chou Harry Chang; Jeng Shyang Pan

doi:10.1109/IIH-MSP.2006.265003

Discriminant feature fusion strategy for supervised learning

Jun Bao Li, Shu Chuan Chu, Jung Chou Harry Chang, Jeng Shyang Pan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.

Original language	English
Title of host publication	Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006
Place of Publication	United States of America
Pages	301-304
Number of pages	4
DOIs	https://doi.org/10.1109/IIH-MSP.2006.265003
Publication status	Published - 1 Dec 2006
Externally published	Yes
Event	2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006 - Pasadena, CA, United States Duration: 18 Dec 2006 → 20 Dec 2006

Publication series

Name	Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006

Conference

Conference	2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006
Country	United States
City	Pasadena, CA
Period	18/12/06 → 20/12/06

Access to Document

10.1109/IIH-MSP.2006.265003Licence: In Copyright

Link to publication in Scopus

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Cite this

Li, J. B., Chu, S. C., Chang, J. C. H., & Pan, J. S. (2006). Discriminant feature fusion strategy for supervised learning. In Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006 (pp. 301-304). [4041723] (Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006).. https://doi.org/10.1109/IIH-MSP.2006.265003

Li, Jun Bao ; Chu, Shu Chuan ; Chang, Jung Chou Harry ; Pan, Jeng Shyang. / Discriminant feature fusion strategy for supervised learning. Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006. United States of America, 2006. pp. 301-304 (Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006).

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title = "Discriminant feature fusion strategy for supervised learning",

abstract = "An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.",

author = "Li, {Jun Bao} and Chu, {Shu Chuan} and Chang, {Jung Chou Harry} and Pan, {Jeng Shyang}",

year = "2006",

month = dec,

day = "1",

doi = "10.1109/IIH-MSP.2006.265003",

language = "English",

isbn = "0769527450",

series = "Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006",

pages = "301--304",

booktitle = "Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006",

note = "2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006 ; Conference date: 18-12-2006 Through 20-12-2006",

}

Li, JB, Chu, SC, Chang, JCH & Pan, JS 2006, Discriminant feature fusion strategy for supervised learning. in Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006., 4041723, Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006, United States of America, pp. 301-304, 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006, Pasadena, CA, United States, 18/12/06. https://doi.org/10.1109/IIH-MSP.2006.265003

Discriminant feature fusion strategy for supervised learning. / Li, Jun Bao; Chu, Shu Chuan; Chang, Jung Chou Harry; Pan, Jeng Shyang.

Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006. United States of America, 2006. p. 301-304 4041723 (Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.

AB - An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.

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Li JB, Chu SC, Chang JCH, Pan JS. Discriminant feature fusion strategy for supervised learning. In Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006. United States of America. 2006. p. 301-304. 4041723. (Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006). https://doi.org/10.1109/IIH-MSP.2006.265003