Abstract
There are many researches on sparse subspace clustering, but there are few related studies on its parameter optimization. In this paper, we propose an adaptive training parameter method to improve the manual selection process of convex optimization regularization parameters and improve the accuracy of subspace clustering. Experiments were carried out on multiple datasets, and the clustering accuracy is improved. The results prove that the improved parameter training process can improve the clustering accuracy of subspace clustering.
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Acknowledgments
Research on this work was partially supported by grants from and National Nature Science Foundation of China (No. 62166028).
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Zhu, K., Li, M. (2022). Sparse Subspace Clustering Based on Adaptive Parameter Training. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_5
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DOI: https://doi.org/10.1007/978-3-031-03948-5_5
Publisher Name: Springer, Cham
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