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. Corisequently, 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 kerriel 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.