A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Mixtures of probabilistic principal component analyzers
Neural Computation
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Distributed data clustering can be efficient and exact
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
Efficient greedy learning of Gaussian mixture models
Neural Computation
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
SMEM Algorithm for Mixture Models
Neural Computation
An experimental comparison of several clustering and initialization methods
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Distributed EM algorithms for density estimation and clustering in sensor networks
IEEE Transactions on Signal Processing
Shared kernel models for class conditional density estimation
IEEE Transactions on Neural Networks
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It has been recently demonstrated that the classical EM algorithm for learning Gaussian mixture models can be successfully implemented in a decentralized manner by resorting to gossip-based randomized distributed protocols. In this paper we describe a gossip-based implementation of an alternative algorithm for learning Gaussian mixtures in which components are added to the mixture one after another. Our new Greedy Gossip-based Gaussian mixture learning algorithm uses gossip-based parallel search, starting from multiple initial guesses, for finding good components to add to the mixture in each component allocation step. It can be executed on massive networks of small computing devices, converging to a solution exponentially faster than its centralized version, while reaching the same quality of generated models.