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Asynchronous distributed principal component analysis using stochastic approximation

Authors: Gemma Morral; Pascal Bianchi; Jérémie Jakubowicz;

Asynchronous distributed principal component analysis using stochastic approximation

Abstract

In this paper we address the problem of asynchronous distributed principal component analysis. We provide several algorithms coping with different situations according to the underlying graph structure. A general enough framework allows us to analyze all these algorithms at the same time. Convergence is proved with probability 1 under suitable assumptions, and numerical experiments illustrate their good behavior.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Average
Average
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