A Review of Distributed Algorithms for Principal Component Analysis

Sissi Xiaoxiao Wu, Hoi To Wai, Lin Li, Anna Scaglione

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


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.

Original languageEnglish (US)
Article number8425655
Pages (from-to)1321-1340
Number of pages20
JournalProceedings of the IEEE
Issue number8
StatePublished - Aug 2018


  • Clustering algorithms
  • data mining
  • distributed algorithms
  • principal component analysis
  • radar signal processing

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering


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