IEEE TRANSACTIONS ON NEURAL NETWORKS,VOL.14,NO.2,MARCH 2003447A Support Vector Machine Formulation to PCA Analysis and Its Kernel VersionJ.A.K.Suykens,T.Van Gestel,J.Vandewalle,and B.De MoorAbstract—In this letter,we present a simple and straightforward primal-dual support vector machine formulation to the problem of principal component analysis (PCA)in dual variables.By considering a mapping to a high-dimensional feature space and application of the kernel trick (Mercer theorem)kernel PCA is obtained as introduced by Schölkopf et al.While least squares support vector machine classifiers have a natural link with kernel Fisher discriminant analysis (minimizing the within class scatter around targets +1andal.[16],The aformulatManusResearch GO A-Me networks under Pr G.0240.9(support A WI (BiSTWW-G managem (flutter m IV-02(1and Con Sustainab Managem Data4s,I the InterdThe aof Electr BelgiumDigital
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