Robust Recovery of Subspace Structures by Low-Rank Representation

Abstract

In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Rank Representation (LRR), which seeks the lowest-rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that LRR well solves the subspace recovery problem: when the data is clean, we prove that LRR exactly captures the true subspace structures; for the data contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for the data corrupted by arbitrary errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace segmentation and error correction, in an efficient way.

IEEE Trans. Pattern Analysis and Machine Intelligence

References in corpus (10)

• The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
• A geometric analysis of subspace clustering with outliers
• Robust Principal Component Analysis?
• Robust PCA via Outlier Pursuit
• Median K-flats for hybrid linear modeling with many outliers
• Spectral clustering based on local linear approximations
• Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation
• Analysis and Improvement of Low Rank Representation for Subspace segmentation
• Foundations of a Multi-way Spectral Clustering Framework for Hybrid Linear Modeling
• TILT: Transform Invariant Low-rank Textures

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