Robust speech recognition using evolutionary class-dependent LDA
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
An Innovative Weighted 2DLDA Approach for Face Recognition
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
High performance classification of two imagery tasks in the cue-based brain computer interface
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Face recognition in global harmonic subspace
IEEE Transactions on Information Forensics and Security
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.