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

页码：2762 / 2767

页数：6

共 50 条

[31] Functional outlier detection with robust functional principal component analysis
Sawant, Pallavi
Billor, Nedret
Shin, Hyejin
COMPUTATIONAL STATISTICS, 2012, 27 (01) : 83 - 102
[32] Robust principal component analysis and outlier detection with ecological data
Jackson, DA
Chen, Y
ENVIRONMETRICS, 2004, 15 (02) : 129 - 139
[33] Evaluating Special Event Transit Demand: A Robust Principal Component Analysis Approach
Kumar, Pramesh
Khani, Alireza
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (12) : 7370 - 7382
[34] A note on robust kernel principal component analysis
Deng, Xinwei
Yuan, Ming
Sudjianto, Agus
PREDICTION AND DISCOVERY, 2007, 443 : 21 - +
[35] Exactly Robust Kernel Principal Component Analysis
Fan, Jicong
Chow, Tommy W. S.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (03) : 749 - 761
[36] Generalized mean for robust principal component analysis
Oh, Jiyong
Kwak, Nojun
PATTERN RECOGNITION, 2016, 54 : 116 - 127
[37] DISTRIBUTIONALLY ROBUST OPTIMIZATION WITH PRINCIPAL COMPONENT ANALYSIS
Cheng, Jianqiang
Chen, Richard Li-Yang
Najm, Habib N.
Pinar, Ali
Safta, Cosmin
Watson, Jean-Paul
SIAM JOURNAL ON OPTIMIZATION, 2018, 28 (02) : 1817 - 1841
[38] ROBUST PRINCIPAL COMPONENT ANALYSIS BY PROJECTION PURSUIT
XIE, YL
WANG, JH
LIANG, YZ
SUN, LX
SONG, XH
YU, RQ
JOURNAL OF CHEMOMETRICS, 1993, 7 (06) : 527 - 541
[39] Principle component analysis: Robust versions
Polyak, B. T.
Khlebnikov, M. V.
AUTOMATION AND REMOTE CONTROL, 2017, 78 (03) : 490 - 506
[40] Robust Orthogonal Complement Principal Component Analysis
She, Yiyuan
Li, Shijie
Wu, Dapeng
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2016, 111 (514) : 763 - 771

← 1 2 3 4 5 →