A Sparse Fixed-Point Online KPCA Extraction Algorithm

Abstract

Kernel principal component analysis (KPCA) is a powerful tool for nonlinear feature extraction, but its standard formulation is not well-suited for streaming data. Although there are efficient online KPCA solutions, there is a gap in the literature regarding genuinely sparse online KPCA algorithms. This paper introduces a novel, fast, and accurate online fixed-point algorithm designed for sparse kernel principal component extraction. Utilizing a two-level sparsifying strategy, the proposed algorithm efficiently handles streaming data and large datasets within minimal computing and memory requirements, achieving both higher accuracy and sparser components compared to existing online KPCA methods.