Subspace Clustering with Block Diagonal Sparse Representation
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11063_2021_10597_ReferencePDF.pdf
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1.47 MB
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Adobe PDF
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080313588b276b6e1a10c1dd259857e3
Author(s)
Fang, Xian
Zhang, Ruixun
Li, Zhengxin
Shao, Xiuli
Date Issued
July 23, 2021
Publisher
Springer US
Citation
Fang, Xian, Zhang, Ruixun, Li, Zhengxin and Shao, Xiuli. 2021. "Subspace Clustering with Block Diagonal Sparse Representation."
Version
Author's final manuscript
Abstract
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.
MIT Department
Sloan School of Management. Laboratory for Financial Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s11063-021-10597-5
