ICML 2019oral24 citations
Noisy Dual Principal Component Pursuit
Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Rene Vidal, Manolis Tsakiris, Daniel Robinson
Abstract
Dual Principal Component Pursuit (DPCP) is a recently proposed non-convex optimization based method for learning subspaces of high relative dimension from noiseless datasets contaminated by as many outliers as the square of the number of inliers. Experimentally, DPCP has proved to be robust to noise and outperform the popular RANSAC on 3D vision tasks such as road plane detection and relative poses estimation from three views. This paper extends the global optimality and convergence theory of DPCP to the case of data corrupted by noise, and further demonstrates its robustness using synthetic and real data.
BibTeX
@InProceedings{pmlr-v97-ding19b,
title = {Noisy Dual Principal Component Pursuit},
author = {Ding, Tianyu and Zhu, Zhihui and Ding, Tianjiao and Yang, Yunchen and Vidal, Rene and Tsakiris, Manolis and Robinson, Daniel},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {1617--1625},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/ding19b/ding19b.pdf},
url = {https://proceedings.mlr.press/v97/ding19b.html},
abstract = {Dual Principal Component Pursuit (DPCP) is a recently proposed non-convex optimization based method for learning subspaces of high relative dimension from noiseless datasets contaminated by as many outliers as the square of the number of inliers. Experimentally, DPCP has proved to be robust to noise and outperform the popular RANSAC on 3D vision tasks such as road plane detection and relative poses estimation from three views. This paper extends the global optimality and convergence theory of DPCP to the case of data corrupted by noise, and further demonstrates its robustness using synthetic and real data.}
}