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In the era of big data, many sparse linear discriminant analysis methods have been proposed for the classification and variable selection of the high-dimensional data. In order to solve the multiclass...
Linear and quadratic discriminant analysis are two very useful classification methods, for which the problem of variable selection is of fundamental impor-tance. To this end, a BIC-type selection crit...
Fisher linear discriminant analysis (LDA) can be sensitive to the problem data. Robust Fisher LDA can systematically alleviate the sensitivity problem by explicitly incorporating a model of data uncer...
Fisher’s linear discriminant analysis (FLDA) is an important dimension reduction method in sta-tistical pattern recognition. It has been shown that FLDA is asymptotically Bayes optimal under the homos...
This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\O$ and the diffe...
In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the prob...
The problem of the non-robustness of the classical estimates in the setting of the quadratic and linear discriminant analysis has been addressed by many authors: Todorov et al. [19, 20], Chork and R...

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