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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2018.2846568