Adaptive incremental principal component analysis in nonstationary online learning environments

In this paper, we propose a new Chunk IPCA algorithm in which an optimal threshold of accumulation ratio is adaptively selected such that the classification accuracy is maximized for a validation data set. In order to obtain a proper set of validation data, an online clustering method called Evolving Clustering Method (ECM) is introduced into Chunk IPCA. In the proposed Chunk IPCA called CIPCA-ECM, training data are first separated into the subsets of every class; then, ECM is applied to each subset to update the validation data set. In the experiments, the evaluation of the proposed Chunk IPCA algorithm is carried out using the four UCI data sets and the effectiveness of updating the threshold is discussed. The results suggest that the incremental learning of an eigenspace in the proposed CIPCA-ECM is stably carried out, and a compact and effective eigenspace is obtained over the entire learning stages. The recognition accuracy of CIPCA-ECM is almost equal to the best performance of CIPCA-FIX in which an optimal threshold is manually predetermined.