Localized feature selection for Gaussian mixtures using variational learning
Yuanhong Li, Ming Dong, Yunqian Ma · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
Typical unsupervised feature selection algorithms select a common feature subset for all the clusters. Consequently, clusters embedded in different feature subspaces are not discovered. 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 real-world datasets illustrate that our approach is superior over both global feature selection and subspace clustering methods.