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Lecture Notes in Computer Science
Kernel principal component analysis (kPCA) has been proposed as a dimensionality-reduction technique that achieves nonlinear, low-dimensional representations of data via the mapping to kernel feature space. Conventionally, kPCA relies on Euclidean statistics in kernel feature space. However, Euclidean analysis can make kPCA inefficient or incorrect for many popular kernels that map input points to a hypersphere in kernel feature space. To address this problem, this paper proposes a noveldoi:10.1007/978-3-662-44848-9_6 fatcat:usqo3y4amnayhj5fldlzaubuke