FCM Classifier for High Dimensional Data

Abstract

A fuzzy classifier based on fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. The FCM classifier uses covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high dimensional data. This paper proposes a way of directly handling high dimensional data in FCM clustering and classification. The proposed classifier outperforms well established relational classifier known as k-nearest neighbor (k-NN) on the benchmark set of COREL image collection, which was used by James Wang for tests of his Simplicity System.