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In this paper, a novel sparse learning method, called sparse local Fisher discriminant analysis (SLFDA) is proposed for facial expression recognition. The SLFDA method is derived from the original local Fisher discriminant analysis (LFDA) and exploits its sparse property. Because the null space of the local mixture scatter matrix of LFDA has no discriminant information, we find the solutions of LFDA...
In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local...
In this paper, we propose a novel feature extraction method called sparse local Fisher discriminant analysis (SLFDA), which is an extension of the local Fisher discriminant analysis (LFDA) algorithm. The proposed method projects the training samples into the range space of local total scatter matrix. Then, it gives the explicit characterization for all solutions of the LFDA. To obtain the sparse projection...
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,...
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