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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2014, Vol. 8 Issue (5) : 785-792    https://doi.org/10.1007/s11704-014-3337-x
RESEARCH ARTICLE
Linear discriminant analysis with worst between-class separation and average within-class compactness
Leilei YANG, Songcan CHEN()
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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Abstract

Linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction (DR) techniques and obtains discriminant projections by maximizing the ratio of average-case between-class scatter to averagecase within-class scatter. Two recent discriminant analysis algorithms (DAS), minimal distance maximization (MDM) and worst-case LDA (WLDA), get projections by optimizing worst-case scatters. In this paper, we develop a new LDA framework called LDA with worst between-class separation and average within-class compactness (WSAC) by maximizing the ratio of worst-case between-class scatter to averagecase within-class scatter. This can be achieved by relaxing the trace ratio optimization to a distance metric learning problem. Comparative experiments demonstrate its effectiveness. In addition, DA counterparts using the local geometry of data and the kernel trick can likewise be embedded into our framework and be solved in the same way.

Keywords dimensionality reduction      linear discriminant analysis      the worst separation      the average compactness     
Corresponding Author(s): Songcan CHEN   
Issue Date: 11 October 2014
 Cite this article:   
Leilei YANG,Songcan CHEN. Linear discriminant analysis with worst between-class separation and average within-class compactness[J]. Front. Comput. Sci., 2014, 8(5): 785-792.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-3337-x
https://academic.hep.com.cn/fcs/EN/Y2014/V8/I5/785
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