Dimensionality reduction in decentralized networks by Gossip aggregation of principal components analyzers - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Dimensionality reduction in decentralized networks by Gossip aggregation of principal components analyzers

Abstract

This paper considers dimensionality reduction in large decentralized networks with limited node-local computing and memory resources and unreliable point-to-point connectivity (e.g peer-to-peer, sensors or ad-hoc mobile networks). We propose an asynchronous decentralized algorithm built on a Gossip consensus protocol that perform Principal Components Analysis (PCA) of data spread over such networks. All nodes obtain the same local basis that span the global principal subspace. Reported experiments show that obtained bases both reach a consensus and accurately estimate the global PCA solution.
Fichier principal
Vignette du fichier
fellus14esann.pdf (124.33 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00985721 , version 1 (30-04-2014)

Identifiers

  • HAL Id : hal-00985721 , version 1

Cite

Jérôme Fellus, David Picard, Philippe-Henri Gosselin. Dimensionality reduction in decentralized networks by Gossip aggregation of principal components analyzers. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2014, Bruges, Belgium. pp.171-176. ⟨hal-00985721⟩
385 View
183 Download

Share

Gmail Facebook X LinkedIn More