A recursive least square algorithm for online kernel principal component extraction

The online extraction of kernel principal components has gained increased attention, and several algorithms proposed recently explore kernelized versions of the generalized Hebbian algorithm (GHA) [1], a well-known principal component analysis (PCA) extraction rule. Consequently, the convergence speed of such algorithms and the accuracy of the extracted components are highly dependent on a proper choice of the learning rate, a problem dependent factor. This paper proposes a new online fixed-point kernel principal component extraction algorithm, exploring the minimization of a recursive least-square error function, conjugated with an approximated deflation transform using component estimates obtained by the algorithm, implicitly applied upon data. The proposed technique automatically builds a concise dictionary to expand kernel components, involves simple recursive equations to dynamically define a specific learning rate to each component under extraction, and has a linear computational complexity regarding dictionary size. As compared to state-of-art kernel principal component extraction algorithms, results show improved convergence speed and accuracy of the components produced by the proposed method in five open-access databases.