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| Research article summary (published 29 Apr 2009): |
Simultaneous localized feature selection and model detection for gaussian mixtures.
Full 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.
Author information
Author/s: Li, Yuanhong (Y); Dong, Ming (M); Hua, Jing (J);
Affiliation: Machine Vision and Pattern Recognition Laboratory, Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. yhli(-atsign-)wayne.edu
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)
Reference: 2009-May; vol 31 (issue 5) : pp 953-60
Dates: Created 2009/03/20; Completed 2009/06/10;
PMID: 19299867, status: MEDLINE (last retrieval date: 6/10/2009, IMS Date: 10 Jun 2009 00:00:00)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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