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- W2186726195 abstract "A framework forclasses ofminorcomponent learning rules ispresented. Intheproposed rules, eigenvectors of a covariance matriax aresimultaneously estimated. The derivation ofMCA rules isbased onoptimizing aweighted inverse Rayleigh quotient sothattheoptimumweights at equilibrium points areexactly thedesired eigenvectors ofa covariance matriax instead ofanarbitrary orthonormal basisoftheminorsubspace. Variations ofthederived MCA learning rules areobtained byimposing orthogonal and quadratic constraints andchange ofvariables. Someof theproposed algorithms canalsoperform PCA bymerely changing thesignofthestep-size. IndexTerms: Minorcomponentanalysis, principal component analysis, adaptive learning algorithm, extreme eigenvalues" @default.
- W2186726195 created "2016-06-24" @default.
- W2186726195 creator A5065143723 @default.
- W2186726195 date "2005-01-01" @default.
- W2186726195 modified "2023-09-26" @default.
- W2186726195 title "Revisiting WeightedInverse Rayleigh Quotientfor MinorComponentExtraction" @default.
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- W2186726195 hasPublicationYear "2005" @default.
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