Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Title:
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Other Titles:
IEEE Transactions on Cybernetics
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Publication Date:
15 March 2016
Citation:
X. Peng; Z. Yu; Z. Yi; H. Tang, "Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering," in IEEE Transactions on Cybernetics , vol.PP, no.99, pp.1-14 doi: 10.1109/TCYB.2016.2536752
Abstract:
Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that ℓ¹⁻, ℓ²⁻, ℓ∞⁻, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.
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PublisherCopyrights
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(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
2168-2267
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