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Principle component analysis: Robust versions

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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.

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Authors and Affiliations

  1. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    B. T. Polyak & M. V. Khlebnikov

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  1. B. T. Polyak
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  2. M. V. Khlebnikov
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Correspondence to B. T. Polyak.

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Original Russian Text © P.T. Polyak, M.V. Khlebnikov, 2017, published in Avtomatika i Telemekhanika, 2017, No. 3, pp. 130–148.

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Polyak, B.T., Khlebnikov, M.V. Principle component analysis: Robust versions. Autom Remote Control 78, 490–506 (2017). https://doi.org/10.1134/S0005117917030092

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  • DOI: https://doi.org/10.1134/S0005117917030092

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