Abstract Independent component analysis (ICA) and linear discriminant analysis (LDA) are two classical feature extraction methods. To extract optimal features, fuzzy technology is introduced into the fusion method of ICA and LDA. The proposed method can extract discriminative features from overlapping (outlier) samples effectively. Firstly, ICA is employed to extract initial features. Then, fuzzy k-nearest neighbor (FKNN) is implemented to achieve the distribution information of original samples. Finally, fuzzy LDA (FLDA) is performed on the basis of the above computation, and the effective feature vectors are extracted. Experimental results on the AR, ORL and NUST603 face databases demonstrate the effectiveness of the proposed method.
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2008, Vol. 21 
