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2008
Springer

An Unsupervised Gaussian Mixture Classification Mechanism Based on Statistical Learning Analysis

8 years 3 months ago
An Unsupervised Gaussian Mixture Classification Mechanism Based on Statistical Learning Analysis
This paper presents a scheme for unsupervised classification with Gaussian mixture models by means of statistical learning analysis. A Bayesian Ying-Yang harmony learning system acts as a statistical tool for the particular derivation and development of automatic joint parameter learning and model selection. The proposed classification mechanism roughly decides on the number of real classes, by earning activation for the winners and assigning penalty for the rivals, so that the most competitive center wins for possible prediction and the extra ones are driven far away when starting the algorithm from a too large number of classes without any prior knowledge. Simulation experiments prove the feasibility of the approach and show good performance for unsupervised classification and natural estimation on the number of classes.
Rui Nian, Guangrong Ji, Michel Verleysen
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Where FSKD
Authors Rui Nian, Guangrong Ji, Michel Verleysen
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