A Review of Distributed Algorithms for Principal Component Analysis

作者：Wu, Sissi Xiaoxiao^*; Wai, Hoi-To; Li, Lin; Scaglione, Anna

来源：Proceedings of the IEEE, 2018, 106(8): 1321-1340.

DOI：10.1109/JPROC.2018.2846568

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

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.

• 出版日期2018-8
• 单位深圳大学