Robust PCA via Dictionary Based Outlier Pursuit

Xingguo Li; Jineng Ren; Sirisha Rambhatla; Yangyang Xu; Jarvis D. Haupt

ICASSP 2018accepted0 citations

Robust PCA via Dictionary Based Outlier Pursuit

Xingguo Li, Jineng Ren, Sirisha Rambhatla, Yangyang Xu, Jarvis D. Haupt

Abstract

In this paper, we examine the problem of locating vector outliers from a large number of inliers, with a particular focus on the case where the outliers are represented in a known basis or dictionary. Using a convex demixing formulation, we provide provable guarantees for exact recovery of the space spanned by the inliers and the supports of the outlier columns, even when the rank of inliers is high and the number of outliers is a constant proportion of total observations. Comprehensive numerical experiments on both synthetic and hyper-spectral imaging real datasets demonstrate the efficiency of our proposed method.

BibTeX

@inproceedings{icassp2018_robustpcaviadict,
  title = {Robust PCA via Dictionary Based Outlier Pursuit},
  author = {Xingguo Li and Jineng Ren and Sirisha Rambhatla and Yangyang Xu and Jarvis D. Haupt},
  booktitle = {ICASSP 2018},
  year = {2018}
}