Robust Principal Component Analysis: An IRLS Approach

被引：6
|
作者：
Polyak, Boris T. [1 ]
Khlebnikov, Mikhail V. [1 ]
机构：
[1] Russian Acad Sci, Inst Control Sci, 65 Profsoyuznaya St, Moscow, Russia
来源：
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
基金：
俄罗斯科学基金会;
关键词：
principal component analysis; robustness; outliers; method of iteratively reweighted least squares; Huber's functions; SYSTEMS;
D O I：
10.1016/j.ifacol.2017.08.585
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The 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 and l(1) techniques. 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. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2762 / 2767
页数：6
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