Learning robust principal components from L1-norm maximization

Ding-cheng FENG; Feng CHEN; Wen-li XU

doi:10.1631/jzus.C1200180

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Learning robust principal components from L1-norm maximization

Computer & Automation | Updated：2022-12-09

- Learning robust principal components from L1-norm maximization
- Learning robust principal components from L1-norm maximization
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 12, Pages: 901-908(2012)
- Affiliations：
  
  1.Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
  2.Department of Automation, Tsinghua University, Beijing 100084, China
- Author bio：
  
  [ "Ding-cheng FENG, fdc08@mails.tsinghua.edu.cn" ]
  Feng CHEN, E-mail: fdc08@mails.tsinghua.edu.cn
  [ "Wen-li XU, xuwl@tsinghua.edu.cn" ]
- Funds：
- DOI：10.1631/jzus.C1200180
  CLC： TP391.4
- Received：11 June 2012，
  
  Revised：2012-11-12，
  
  Published：2012-12
- Accepted：
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Ding-cheng FENG, Feng CHEN, Wen-li XU. Learning robust principal components from L1-norm maximization[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(12): 901-908.
DOI：

Ding-cheng FENG, Feng CHEN, Wen-li XU. Learning robust principal components from L1-norm maximization[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(12): 901-908. DOI： 10.1631/jzus.C1200180.

摘要

Abstract

Principal component analysis (PCA) is fundamental in many pattern recognition applications. Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization (L1-PCA-REM) since conventional L2-norm based PCA (L2-PCA) is sensitive to outliers. Recently

the variance maximization formulation of PCA with L1-norm (L1-PCA-VM) has been proposed

where new greedy and non greedy solutions are developed. Armed with the gradient ascent perspective for optimization

we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation

which are verified by experiments on synthetic and real-world datasets.

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Keywords

references

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