Factor Analysis Latent Subspace Modeling and Robust Fuzzy Clustering Using t-Distributions
Factor analysis is a latent subspace model commonly used for local dimensionality reduction tasks. Fuzzy c-means (FCM) type fuzzy clustering approaches are closely related to Gaussian mixture models (GMMs), and expectation-maximization (EM) like algorithms have been employed in fuzzy clustering with regularized objective functions. Student's t-mixture models (SMMs) have been proposed recently as an alternative to GMMs, resolving their outlier vulnerability problems. In this paper, we propose a novel FCM-type fuzzy clustering scheme providing two significant benefits when compared with the existing approaches. First, it provides a well-established observation space dimensionality reduction framework for fuzzy clustering algorithms based on factor analysis, allowing concurrent performance of fuzzy clustering and, within each cluster, local dimensionality reduction. Second, it exploits the outlier tolerance advantages of SMMs to provide a novel, soundly founded, nonheuristic, robust fuzzy clustering framework by introducing the effective means to incorporate the explicit assumption about Student's t-distributed data into the fuzzy clustering procedure. This way, the proposed model yields a significant performance increase for the fuzzy clustering algorithm, as we experimentally demonstrate.
factor analysis fuzzy clustering student's t-distribution kullback-leibler divergence local subspace modeling