Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering release_ied45xcjmbhllmqdlku6pqattq

by Xi Peng, Zhiding Yu, Huajin Tang, Zhang Yi

Released as a article .

2012  

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., intra-subspace 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 ℓ_1-, ℓ_2-, ℓ_∞-, and nuclear-norm based linear projection spaces share the property of Intra-subspace Projection Dominance (IPD), i.e., the coefficients over intra-subspace data points are larger than those over inter-subspace 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. Experiments show that L2-Graph algorithms outperform the state-of-the-art methods for feature extraction, image clustering, and motion segmentation in terms of accuracy, robustness, and time efficiency.
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Type  article
Stage   submitted
Date   2012-11-08
Version   v3
Language   en ?
arXiv  1209.0841v3
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