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Neural Processing Letters

Erschienen in:

11.08.2018

An Improved Structured Low-Rank Representation for Disjoint Subspace Segmentation

verfasst von: Lai Wei, Yan Zhang, Jun Yin, Rigui Zhou, Changming Zhu, Xiafeng Zhang

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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Abstract

Low-rank representation (LRR) and its extensions have shown prominent performances in subspace segmentation tasks. Among these algorithms, structured-constrained low-rank representation (SCLRR) is proved to be superior to classical LRR because of its usage of structure information of data sets. Compared with LRR, in the objective function of SCLRR, an additional constraint term is added to compel the obtained coefficient matrices to reveal the subspace structures of data sets more precisely. However, it is very difficult to determine the best value for the corresponding parameter of the constraint term, and an improper value will decrease the performance of SCLRR sharply. For the sake of alleviating the problem in SCLRR, in this paper, we proposed an improved structured low-rank representation (ISLRR). Our proposed method introduces the structure information of data sets into the equality constraint term of LRR. Hence, ISLRR avoids the adjustment of the extra parameter. Experiments conducted on some benchmark databases showed that the proposed algorithm was superior to the related algorithms.

Vorheriger Artikel Bifurcation Analysis of Delayed Complex-Valued Neural Network with Diffusions

Nächster Artikel Synchronization for Nonlinear Complex Spatio-Temporal Networks with Multiple Time-Invariant Delays and Multiple Time-Varying Delays

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Titel: An Improved Structured Low-Rank Representation for Disjoint Subspace Segmentation
verfasst von: Lai Wei
Yan Zhang
Jun Yin
Rigui Zhou
Changming Zhu
Xiafeng Zhang
Publikationsdatum: 11.08.2018
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-018-9901-x

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