Incremental principal component analysis based on adaptive accumulation ratio

Abstract

We have proposed an online feature extraction method called Chunk Incremental Principal Component Analysis (Chunk IPCA) where a chunk of data is trained at a time to update an eigenspace model. In this paper, we propose an extended version of Chunk IPCA in which a proper threshold for the accumulation ratio is adaptively determined such that the highest classification accuracy is maintained for a validation data set. Whenever a new chunk of training data is given, the validation set is updated in an online fashion by using the k-means clustering or through the prototype selection based on the classification results. The experimental results show that the extended version of Chunk IPCA can determine a proper threshold on an ongoing basis, resulting in keeping higher classification accuracy than the original Chunk IPCA. © 2009 Springer Berlin Heidelberg.