.png){kind=icon}
.png){kind=icon}
Return
Asynchronous gossip principal components analysis
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2014.11.076.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Distributed machine learning
Dimensionality reduction
Gossip protocols
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
No organization information available