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Avtomatika i Telemekhanika, 2017, Issue 3, Pages 130–148 (Mi at14465)  
This article is cited in 25 scientific papers (total in 25 papers)

Data Analysis

Principle component analysis: robust versions

B. T. Polyak, M. V. Khlebnikov

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
Full-text PDF (1155 kB) Citations (25)
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Abstract: Modern problems of optimization, estimation, signal and image processing, pattern recognition, etc., deal with huge-dimensional data; this necessitates elaboration of efficient methods of processing such data. The idea of building low-dimensional approximations to huge data arrays is in the heart of the modern data analysis.
One of the most appealing methods of compact data representation is the statistical method referred to as the principal component analysis; however, it is sensitive to uncertainties in the available data and to the presence of outliers. In this paper, robust versions of the principle component analysis approach are proposed along with numerical methods for their implementation.
Keywords: principal component analysis, iteratively reweighted least squares, contaminated Gaussian distribution, outliers, robustness.
Funding agency Grant number
Russian Science Foundation 16-11-10015
This work was supported by the Russian Scientific Foundation, project no. 16-11-10015.
Presented by the member of Editorial Board: A. I. Kibzun

Received: 30.05.2016
English version:
Automation and Remote Control, 2017, Volume 78, Issue 3, Pages 490–506
DOI: https://doi.org/10.1134/S0005117917030092
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: B. T. Polyak, M. V. Khlebnikov, “Principle component analysis: robust versions”, Avtomat. i Telemekh., 2017, no. 3, 130–148; Autom. Remote Control, 78:3 (2017), 490–506
Citation in format AMSBIB
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  • https://www.mathnet.ru/eng/at/y2017/i3/p130
    • This publication is cited in the following 25 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
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    Full-text PDF :285
    References:100
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