Reference : Efficient Greedy Learning of Gaussian Mixture Models
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/11062
Efficient Greedy Learning of Gaussian Mixture Models
English
Verbeek, J. J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Kröse, B. [> >]
2003
Neural Computation
MIT Press
15
2
469-485
Yes (verified by ORBilu)
0899-7667
1530-888X
Cambridge
United Kingdom
[en] This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one aftertheother.We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into the existing mixture. The resulting algorithm resolves the sensitivity to initialization of state-of-the-art methods, like expectation maximization, and has running time linear in the number of data points and quadratic in the (final) number of mixture components. Due to its greedy nature, the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algorithm to other methods on density estimation and texture segmentation are provided.
http://hdl.handle.net/10993/11062

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