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Multi-geometric Sparse Subspace Clustering

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Abstract

Recently, the Riemannian manifold has received special attention in unsupervised clustering since the real-world visual data usually resides on a special manifold where Euclidean geometry fails to capture. Although many clustering algorithms have been proposed, most of them use only a single geometric model to describe the data. In this paper, a multi-geometric subspace clustering model is proposed, and the subspace representation is learned together by constructing a shared affinity matrix of multi-order data. Experimental results on several different types of datasets show that the clustering performance of our proposed algorithm is better than most of subspaces algorithms.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (Grant Nos. 61672265, U1836218), the 111 Project of Ministry of Education of China (Grant No. B12018).

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China

    Wen-Bo Hu

  2. School of IoT Engineering, Jiangnan University, Wuxi, 214122, China

    Xiao-Jun Wu

  3. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Wuxi, Jiangsu Province, China

    Wen-Bo Hu & Xiao-Jun Wu

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  1. Wen-Bo Hu
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  2. Xiao-Jun Wu
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Correspondence to Xiao-Jun Wu.

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Hu, WB., Wu, XJ. Multi-geometric Sparse Subspace Clustering. Neural Process Lett 52, 849–867 (2020). https://doi.org/10.1007/s11063-020-10274-z

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