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Elements of information theory
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Estimation of mixture models
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Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Learning Gaussian mixture models with entropy-based criteria
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QML-based joint diagonalization of positive-definite hermitian matrices
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Text classification using web corpora and EM algorithms
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Random swap EM algorithm for Gaussian mixture models
Pattern Recognition Letters
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Pattern Recognition
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Signal Processing
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This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one after the other. 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.