A Review of Distributed Algorithms for Principal Component Analysis

Principal component analysis (PCA) is a fundamental primitive of many data analysis, array processing, and machine learning methods. In applications where extremely large arrays of data are involved, particularly in distributed data acquisition systems, distributed PCA algorithms can harness local c...

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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 106; no. 8; pp. 1321 - 1340
Main Authors: Wu, Sissi Xiaoxiao, Wai, Hoi-To, Li, Lin, Scaglione, Anna
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Arrays
Clustering algorithms
Communication
Covariance
Data acquisition systems
Data analysis
data mining
Distributed algorithms
Distributed databases
Machine learning
Machine learning algorithms
Mathematical analysis
Principal component analysis
Principal components analysis
radar signal processing
Signal processing algorithms
Statistical analysis
ISSN:0018-9219, 1558-2256
Online Access:Get full text
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Summary:Principal component analysis (PCA) is a fundamental primitive of many data analysis, array processing, and machine learning methods. In applications where extremely large arrays of data are involved, particularly in distributed data acquisition systems, distributed PCA algorithms can harness local communications and network connectivity to overcome the need of communicating and accessing the entire array locally. A key feature of distributed PCA algorithm is that they defy the conventional notion that the first step toward computing the principal vectors is to form a sample covariance. This paper is a survey of the methodologies to perform distributed PCA on different data sets, their performance, and of their applications in the context of distributed data acquisition systems.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2018.2846568