Fast Parallel Expectation Maximization for Gaussian Mixture Models on GPUs Using CUDA

2009 ◽  
Author(s):  
N. S. L. Phani Kumar ◽  
Sanjiv Satoor ◽  
Ian Buck
Keyword(s):  
Mixture Models ◽  
Expectation Maximization ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture

scholarly journals Efficient Greedy Learning of Gaussian Mixture Models

2003 ◽  
Vol 15 (2) ◽  
pp. 469-485 ◽  
Author(s):  
J. J. Verbeek ◽  
N. Vlassis ◽  
B. Kröse
Keyword(s):  
Mixture Models ◽  
Density Estimation ◽  
Expectation Maximization ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Gaussian Mixtures ◽  
Data Points ◽  
Time Linear

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.

Evolving Gaussian Mixture Models with Splitting and Merging Mutation Operators

2016 ◽  
Vol 24 (2) ◽  
pp. 293-317 ◽  
Author(s):  
Thiago Ferreira Covões ◽  
Eduardo Raul Hruschka ◽  
Joydeep Ghosh
Keyword(s):  
Em Algorithm ◽  
Mixture Models ◽  
Expectation Maximization ◽  
Complexity Analysis ◽  
A Priori ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Critical Parameters ◽  
Mutation Operators ◽  
Split And Merge

This paper describes the evolutionary split and merge for expectation maximization (ESM-EM) algorithm and eight of its variants, which are based on the use of split and merge operations to evolve Gaussian mixture models. Asymptotic time complexity analysis shows that the proposed algorithms are competitive with the state-of-the-art genetic-based expectation maximization (GA-EM) algorithm. Experiments performed in 35 data sets showed that ESM-EM can be computationally more efficient than the widely used multiple runs of EM (for different numbers of components and initializations). Moreover, a variant of ESM-EM free from critical parameters was shown to be able to provide competitive results with GA-EM, even when GA-EM parameters were fine-tuned a priori.

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