Details

Autor(en) / Beteiligte

• Verbeek, J. J.
• Vlassis, N.
• Kröse, B.

Titel

Efficient Greedy Learning of Gaussian Mixture Models

Ist Teil von

• Neural computation, 2003-02, Vol.15 (2), p.469-485

Ort / Verlag

One Rogers Street, Cambridge, MA 02142-1209, USA: MIT Press

Erscheinungsjahr

2003

Quelle

EBSCOhost Psychology and Behavioral Sciences Collection

Beschreibungen/Notizen

• 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.