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

Abstract

AbstractWe 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 shoule be 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, to use this ratio as a criterion, we also need to develop a one-pass update algorithm for the 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 ECM is adopted as a prototype learning method in Nearest Neighbor classifier.KeywordsFeature SpaceTraining SampleRecognition AccuracyKernel Principal Component AnalysisAccumulation RatioThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.