Asynchronous gossip principal components analysis

[摘要] This paper deals with Principal Components Analysis (PCA) of data spread over a network where central coordination and synchronous communication between networking nodes are forbidden. We propose an asynchronous and decentralized PCA algorithm dedicated to large scale problems, where large simultaneously applies to dimensionality, number of observations and network size. It is based on the integration of a dimension reduction step into a gossip consensus protocol. Unlike other approaches, a straightforward dual formulation makes it suitable when observed dimensions are distributed. We theoretically show its equivalence with a centralized PCA under a low-rank assumption on training data. An experimental analysis reveals that it achieves a good accuracy with a reasonable communication cost even when the low-rank assumption is relaxed. (C) 2015 Elsevier B.V. All rights reserved.

[发布日期] 2015-12-02

[效力级别]  Proceedings Paper

[关键词] Distributed machine learning;Dimensionality reduction;Gossip protocols