文章基本信息

标题：Robust Principal Component Analysis: An IRLS Approach * * This work was supported by the Russian Scientific Foundation, project no. 16-11-10015.
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作者：Boris T. Polyak ; Mikhail V. Khlebnikov
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：2762-2767
DOI：10.1016/j.ifacol.2017.08.585
语种：English
出版社：Elsevier
摘要：AbstractThe modern problems of optimization, estimation, signal processing, and image recognition deal with data of huge dimensions. It is important to develop effective methods and algorithms for such problems. An important idea is the construction of low-dimension approximations to large-scale data. One of the most popular methods for this purpose is the principal component analysis (PCA), which is, however, sensitive to outliers. There exist numerous robust versions of PCA, relying on sparsity ideas andℓ1techniques. The present paper offers another approach to robust PCA exploiting Huber’s functions and numerical implementation based on the Iterative Reweighted Least Squares (IRLS) method.
关键词：Keywordsprincipal component analysisrobustnessoutliersmethod of iteratively reweighted least squaresHuber’s functions