Feature clustering dimensionality reduction based on affinity propagation

Feature clustering is a powerful technique for dimensionality reduction. However, existing approaches require the number of clusters to be given in advance or controlled by parameters. In this paper, by combining with affinity propagation (AP), we propose a new feature clustering (FC) algorithm, called APFC, for dimensionality reduction. For a given training dataset, the original features automatically form a bunch of clusters by AP. A new feature can then be extracted from each cluster in three different ways for reducing the dimensionality of the original data. APFC requires no provision of the number of clusters (or extracted features) beforehand. Moreover, it avoids computing the eigenvalues and eigenvectors of covariance matrix which is often necessary in many feature extraction methods. In order to demonstrate the effectiveness and efficiency of APFC, extensive experiments are conducted to compare it with three well-established dimensionality reduction methods on 14 UCI datasets in terms of classification accuracy and computational time.