Robust Subspace Segmentation by Low-Rank Representation
Abstract
We propose low-rank representation(LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowest-rank representation among all the candidates that represent all vectors as the linear combination of the bases in a dictionary. Unlike the well-known sparse representation (SR), which computes the sparsest representation of each data vector individually, LRR aims at finding the lowest-rank representation of a collection of vectors jointly. LRR better captures the global structure of data, giving a more effective tool for robust subspace segmentation from corrupted data. Both theoretical and experimental results show that LRR is a promising tool for subspace segmentation.
Cite
Text
Liu et al. "Robust Subspace Segmentation by Low-Rank Representation." International Conference on Machine Learning, 2010.Markdown
[Liu et al. "Robust Subspace Segmentation by Low-Rank Representation." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/liu2010icml-robust/)BibTeX
@inproceedings{liu2010icml-robust,
title = {{Robust Subspace Segmentation by Low-Rank Representation}},
author = {Liu, Guangcan and Lin, Zhouchen and Yu, Yong},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {663-670},
url = {https://mlanthology.org/icml/2010/liu2010icml-robust/}
}