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ICASSP
2010
IEEE

Hierarchical Gaussian Mixture Model

8 years 9 months ago
Hierarchical Gaussian Mixture Model
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually complex and further need to be simplified. In this paper, we present a GMM simplification method based on a hierarchical clustering algorithm. Our method allows one to first to quickly compute a compact version of the initial GMM, and second to automatically learn the optimal number of components of the simplified GMM. Using the framework of Bregman divergences, this simplification algorithm, although presented here for GMMs, is suitable for any mixture of exponential families.
Vincent Garcia, Frank Nielsen, Richard Nock
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Vincent Garcia, Frank Nielsen, Richard Nock
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