Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/238
Title: An improved LDA approach
Authors: Jing, Xiao-Yuan
Zhang, David D.
Tang, Yuan-Yan
Subjects: Discrimination vectors selection
Fisherface method
Image recognition
Improved linear discrimination analysis (ILDA) approach
Statistical uncorrelation
Principal components selection
Issue Date: Oct-2004
Publisher: IEEE
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Oct. 2004, v. 34, no. 5, p.1942-1951.
Abstract: Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vectors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synthesizes these improvements. Experimental results on different image databases demonstrate that our improvements on LDA are efficient, and that ILDA outperforms other state-of-the-art linear discrimination methods.
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Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/238
DOI: 10.1109/TSMCB.2004.831770
ISSN: 1083-4419
Appears in Collections:COMP Journal/Magazine Articles

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