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Neural Processing Letters

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23-07-2021

Subspace Clustering with Block Diagonal Sparse Representation

Authors: Xian Fang, Ruixun Zhang, Zhengxin Li, Xiuli Shao

Published in: Neural Processing Letters | Issue 6/2021

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Abstract

Structured representation is of remarkable significance in subspace clustering. However, most of the existing subspace clustering algorithms resort to single-structured representation, which may fail to fully capture the essential characteristics of data. To address this issue, a novel multi-structured representation subspace clustering algorithm called block diagonal sparse representation (BDSR) is proposed in this paper. It takes both sparse and block diagonal structured representations into account to obtain the desired affinity matrix. The unified framework is established by integrating the block diagonal prior into the original sparse subspace clustering framework and the resulting optimization problem is iteratively solved by the inexact augmented Lagrange multipliers (IALM). Extensive experiments on both synthetic and real-world datasets well demonstrate the effectiveness and efficiency of the proposed algorithm against the state-of-the-art algorithms.

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Title: Subspace Clustering with Block Diagonal Sparse Representation
Authors: Xian Fang
Ruixun Zhang
Zhengxin Li
Xiuli Shao
Publication date: 23-07-2021
Publisher: Springer US
Published in: Neural Processing Letters / Issue 6/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-021-10597-5

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