Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
A procedure for the detection of multivariate outliers
Computational Statistics & Data Analysis
Mixtures of probabilistic principal component analyzers
Neural Computation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Robust mixture modelling using the t distribution
Statistics and Computing
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
From a Gaussian mixture model to additive fuzzy systems
IEEE Transactions on Fuzzy Systems
Regularized Linear Fuzzy Clustering and Probabilistic PCA Mixture Models
IEEE Transactions on Fuzzy Systems
Soft fuzzy rough sets for robust feature evaluation and selection
Information Sciences: an International Journal
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A possibilistic clustering approach toward generative mixture models
Pattern Recognition
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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 effectivemeans 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.