Accelerated Greedy Mixture Learning

Accelerated EM - based clustering of large data sets 1

Motivated by the poor performance (linear complexity) of the EM algorithm 5 in clustering large data sets, and inspired by the successful accelerated versions of related 6 algorithms like k-means, we derive an accelerated variant of the EM algorithm for Gaussian 7 mixtures that: (1) offers speedups that are at least linear in the number of data points, (2) 8 ensures convergence by strictly incr...

Efficient greedy learning of Gaussian mixture models

Efficient greedy learning of Gaussian mixture models

Gossip-Based Greedy Gaussian Mixture Learning

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 ano...

Efficient Greedy Learning of Gaussian Mixture Models

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 th...