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In this paper, we propose a novel feature extraction method called double sparse local Fisher discriminant analysis (DSLFDA), which is an extension of the local Fisher discriminant analysis (LFDA) algorithm. The proposed method combines the idea of sparse representation to construct an adaptive graph to describe the structure information of the samples. Meanwhile, to obtain the sparse projection vectors,...
In recent years, feature extraction method make an achievement in pattern recognition. It extracts not only useful feature for classification, but also reduces the dimension of pattern sample. Linear discriminant analysis is an important method for image recognition, it achieve significant development both in theory and applications. Local fisher discriminant analysis redefines the between-class and...
Automatic gender classification aims at analyzing the face image to recognize gender with computer, in which feature extraction is one key step. The LBP (local binary pattern) feature has essential applications in face analysis and has been applied in gender recognition. The normally adopted LBP feature will encounter dimension explosion with the increase of sampling density of LBP operator, which...
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