title: Simultaneous localized feature selection and model detection for gaussian mixtures.
abstract: 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.
authors.affiliation: Machine Vision and Pattern Recognition Laboratory, Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. yhli@wayne.edu
authors.forename: Yuanhong
authors.initials: Y
authors.lastname: Li
authors.forename: Ming
authors.initials: M
authors.lastname: Dong
authors.forename: Jing
authors.initials: J
authors.lastname: Hua
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*