Abstract
Graph Laplacian methods are shown to be effective to reveal low-dimensional manifold which concealed in high-dimensional data features. However, the quality of constructed graph affects the final performance of graph Laplacian similarity. In this paper, we propose a low-rank Laplacian similarity learning method with local reconstruction restriction and selection operator type minimization. A low-rank constraint is added to the graph Laplacian matrix. An iterative algorithm is proposed to optimize the low-rank Laplacian similarity learning method. The proposed method is applied to clustering, classification and semi-supervised classification. Extensive experiments demonstrate the effectiveness of the proposed method.
This work was supported in part by NSFC Key Projects of International (Regional) Cooperation and Exchanges under Grant 61860206004 and in part by Shenzhen Science & Research Project under Grant JCYJ20170817155854115.
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Chen, SB., Wang, RR., Luo, B., Zhang, J. (2020). Low-Rank Laplacian Similarity Learning. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_4
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