References of "Kowalczyk, W"
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See detailGossip-based greedy Gaussian mixture learning
Vlassis, Nikos UL; Sfakianakis, Y.; Kowalczyk, W.

in Lecture Notes in Computer Science (2005)

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 ... [more ▼]

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. [less ▲]

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See detailNewscast EM
Kowalczyk, W.; Vlassis, Nikos UL

in Advances in Neural Information Processing Systems 17 (2005)

We propose a gossip-based distributed algorithm for Gaussian mixture learning, Newscast EM. The algorithm operates on network topologies where each node observes a local quantity and can communicate with ... [more ▼]

We propose a gossip-based distributed algorithm for Gaussian mixture learning, Newscast EM. The algorithm operates on network topologies where each node observes a local quantity and can communicate with other nodes in an arbitrary point-to-point fashion. The main difference between Newscast EM and the standard EM algorithm is that the M-step in our case is implemented in a decentralized manner: (random) pairs of nodes repeatedly exchange their local parameter estimates and combine them by (weighted) averaging. We provide theoretical evidence and demonstrate experimentally that, under this protocol, nodes converge exponentially fast to the correct estimates in each M-step of the EM algorithm. [less ▲]

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