A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis

Abstract

Kernel supervised principal component analysis (KSPCA) is a computationally efficient supervised feature extraction method that can learn non-linear transformations. We start the study of the statistical properties of KSPCA, providing the first bound on its sample complexity. This bound is dimension-independent, which justifies the good performance of KSPCA… (More)

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