An entropy weighting mixture model for subspace clustering of high-dimensional data
Pattern Recognition Letters
Simultaneous model selection and feature selection via BYY harmony learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Model-based multidimensional clustering of categorical data
Artificial Intelligence
Expert Systems with Applications: An International Journal
Journal of Visual Communication and Image Representation
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Semi-supervised projected model-based clustering
Data Mining and Knowledge Discovery
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In this paper, we propose a novel approach of simultaneous localized feature selection and model detection for unsupervised learning. In our approach, local feature saliency, together with other parameters of Gaussian mixtures, are estimated by Bayesian variational learning. Experiments performed on both synthetic and real-world data sets demonstrate that our approach is superior over both global feature selection and subspace clustering methods.