Matrix-based Kernel Principal Component analysis for large-scale data set

Abstract

Kernel Principal Component Analysis (KPCA) is a nonlinear feature extraction approach, which generally needs to eigen-decompose the kernel matrix. But the size of kernel matrix scales with the number of data points, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set. To overcome computational and storage problem… (More)
DOI: 10.1109/IJCNN.2009.5178692

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