A Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier

[Abstract] We have proposed a new concept for pattern classification systems in which feature selection and classifier learning are simultaneously carried out on-line. To realize this concept, Incremental Principal Component Analysis (IPCA) and Evolving Clustering Method (ECM) was effectively combined in the previous work. However, in order to construct a desirable feature space, a threshold value to determine the increase of a new feature is properly given in the original IPCA. To alleviate this problem, we can adopt the accumulation ratio as its criterion. However, in incremental situations, the accumulation ratio must be modified every time a new sample is given. Therefore, in order to use this ratio as a criterion, we also need to develop a one-pass update algorithm for this ratio. In this paper, we propose an improved algorithm of IPCA in which the accumulation ratio as well as the feature space can be updated on-line without all the past samples. To see if correct feature construction is carried out by this new IPCA algorithm, the recognition performance is evaluated for some standard datasets when Evolving Clustering Method (ECM) is adopted as a prototype learning method in Nearest Neighbor classifier.