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One-step kernelized sparse clustering on grassmann manifolds

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Abstract

Sparse Subspace Clustering (SSC) based clustering methods have achieved great success since these methods could effectively explore the low-dimensional subspace structure embedded in the original data. However, most existing subspace clustering methods are designed for vectorial data from linear spaces, thus not suitable for high dimensional data (such as imageset or video) with the non-linear manifold structure. In this paper, we propose a unified framework about kernelized sparse subspace clustering on Grassmann manifolds, which can learn the optimal affinity graph with the best clustering index matrix. The experimental results on six public datasets illustrate that the proposed method is obviously better than most related clustering methods based on Grassmann manifolds.

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Notes

  1. This new dataset can be downloaded from our website: https://drive.google.com/drive/folders/1iLU-FKmx8AXXW_DP6tyHJ636Nb_qvq2V?usp=sharing

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62020106012, U1836218, 61672265), and the 111 Project of Ministry of Education of China (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, Jiangnan, China

    Wen-Bo Hu & Xiao-Jun Wu

  4. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK

    Tian-Yang Xu

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  1. Wen-Bo Hu
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  2. Xiao-Jun Wu
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  3. Tian-Yang Xu
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Correspondence to Xiao-Jun Wu.

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Hu, WB., Wu, XJ. & Xu, TY. One-step kernelized sparse clustering on grassmann manifolds. Multimed Tools Appl 81, 31017–31038 (2022). https://doi.org/10.1007/s11042-022-12495-x

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