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Typically, two aspects are used to evaluate the quality of a classification model, i.e., the classifying accuracy and the interpretability. The recently developed sparse representation-based face recognition techniques, though achieving high accuracies, rarely concern the interpretability of the classification model. In particular, the obtained sparseness, in terms of the sparse representative coefficient...
In this paper, a kernel uncorrelated adjacent-class discriminant analysis (KUADA) approach is proposed for image recognition. The optimal nonlinear discriminant vector obtained by this approach can differentiate one class and its adjacent classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices in kernel space using the Fisher criterion...
This paper presents a new nonparametric linear feature extraction method coined geometrically intuitive marginal discriminant analysis (IMDA). Motivated by the law of cosines in trigonometry, we characterize the square local margin by a weighted difference of the square between-class distance and the square within-class distance. Based on this characterization, we design a class margin criterion which...
In this paper, we propose a novel method for feature extraction and recognition, namely, complete fuzzy LDA (CFLDA). CFLDA combines the complete LDA and fuzzy set theory. CFLDA redefines the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix that make fully of the distribution of sample and simultaneously extract the irregular discriminative information and regular discriminative...
Maximum margin criterion (MMC) based feature extraction method is more efficient than LDA for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local structures of samples. In this paper, we develop a novel criterion to address the issue, namely local maximum margin criterion...
In this paper, a novel linear projection classification technique, termed regularized large margin classifier (RIMC), is developed in this paper. Through adding a regularized term (within-class scatter) to the margin criterion of the classical large margin classifier, the margin between two classes of the resulting projection feature vectors achieves maximized while the within-class scatter can achieve...
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