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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Veröffentlicht in:	Proceedings of the IEEE Jg. 106; H. 8; S. 1321 - 1340
Hauptverfasser:	Wu, Sissi Xiaoxiao, Wai, Hoi-To, Li, Lin, Scaglione, Anna
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Li, Lin Wai, Hoi-To Scaglione, Anna Wu, Sissi Xiaoxiao
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SubjectTerms	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
Title	A Review of Distributed Algorithms for Principal Component Analysis
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