Adaptive subset kernel principal component analysis for time-varying patterns

Kernel principal component analysis (KPCA) and its online learning algorithms have been proposed and widely used. Since KPCA uses training samples for bases of the operator, its online learning algorithms require the preparation of all training samples beforehand. Subset KPCA (SubKPCA), which uses a subset of samples for the basis set, has been proposed and has demonstrated better performance with less computational complexity. In this paper, we extend SubKPCA to an online version and propose methods to add and exchange a sample in the basis set. Since the proposed method uses the basis set, we do not need to prepare all training samples beforehand. Therefore, the proposed method can be applied to time-varying patterns, in contrast to existing online KPCA algorithms. Experimental results demonstrate the advantages of the proposed method.