An Expectation–Maximization algorithm for the Wishart mixture model: Application to movement clustering

This article presents an Expectation–Maximization algorithm for the Wishart mixture model in which realizations are matrices. Given a set of matrices, an iterative algorithm for estimating the parameters of such a mixture model is proposed. The obtained estimates can be interpreted in terms of mean...

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Veröffentlicht in:	Pattern recognition letters Jg. 31; H. 14; S. 2318 - 2324
Hauptverfasser:	Hidot, Sullivan, Saint-Jean, Christophe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.10.2010 Elsevier
Schlagworte:	Applied sciences Clustering Computer Science Detection, estimation, filtering, equalization, prediction EM algorithm Engineering Sciences Exact sciences and technology Information, signal and communications theory Movement recognition Second-order cross moments Signal and communications theory Signal and Image Processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Wishart mixture model Second-order cross moments Wishart mixture model Movement recognition Clustering EM algorithm Second order Automatic classification Parameter estimation Mixture theory Iterative method Scale factor Signal classification A posteriori estimation Geometrical method
ISSN:	0167-8655, 1872-7344
Online-Zugang:	Volltext
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