Algorithms for clustering data
Algorithms for clustering data
Introduction to Monte Carlo methods
Learning in graphical models
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Learning Gaussian mixture models with entropy-based criteria
IEEE Transactions on Neural Networks
Variational Bayesian mixture model on a subspace of exponential family distributions
IEEE Transactions on Neural Networks
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Variational learning for Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Unsupervised Learning of Gaussian Mixtures Based on Variational Component Splitting
IEEE Transactions on Neural Networks
From points to nodes: inverse graph embedding through a lagrangian formulation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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In this paper, we propose a fast entropy-based variational scheme for learning Gaussian mixtures. The key element of the proposal is to exploit the incremental learning approach to perform model selection through efficient iteration over the Variational Bayes (VB) optimization step in a way that the number of splits is minimized. In order to minimize the number of splits we only select for spliting the worse kernel in terms of evaluating its entropy. Recent Gaussian mixture learning proposals suggest the use of that mechanism if a bypass entropy estimator is available. Here we will exploit the recently proposed Leonenko estimator. Our experimental results, both in 2D and in higher dimension show the effectiveness of the approach which reduces an order of magnitude the computational cost of the state-of-the-art incremental component learners.