Perturbation scheme for online learning of features: Incremental principal component analysis

A new incremental online feature extraction approach is proposed based on principal component analysis in conjunction with perturbation theory which for its validity requires the perturbation parameter to be small. Our approach is found to be computationally more efficient in comparison to batch method which for its applicability requires simultaneous availability of all observations for computation of features. It is found on the basis of numerical experiments that the results based on our approach besides being in good agreement with the batch method and other incremental methods are also computationally more efficient. To demonstrate the efficacy of the proposed scheme, experiments have been performed on randomly generated datasets as well as on low and high dimensional datasets, i.e. UCI and face datasets which are available in public domain.