Robust Subspace Segmentation by Low-Rank Representation

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 lowestrank 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.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Guangcan Liu, Zhouchen Lin, Yong Yu
• Keywords: Data Vector, ICML 2010, Low-rank Representation, Machine Learning, Subspace Segmentation