Abstract
The affinity matrix is a key in designing different subspace clustering methods. Many existing methods obtain correct clustering by indirectly pursuing block-diagonal affinity matrix. In this paper, we propose a novel subspace clustering method, called rank-constrained block diagonal representation (RCBDR), for subspace clustering. RCBDR method benefits mostly from three aspects: (1) the block diagonal affinity matrix is directly pursued by inducing rank constraint to Laplacian regularizer; (2) RCBDR guarantees not only between-cluster sparsity because of its block diagonal property, but also preserves the within-cluster correlation by considering the Frobenius norm of coefficient matrix; (3) a simple and efficient solver for RCBDR is proposed. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of the proposed algorithm.
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Acknowledgement
This work was supported by the Scientific Research Plan Projects of Shaanxi Education Department (No.17JK0610); the Doctoral Scientific Research Foundation of Shaanxi Province (0108-134010006).
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Yang, Y., Jie, Z. (2019). Rank-Constrained Block Diagonal Representation for Subspace Clustering. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_46
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DOI: https://doi.org/10.1007/978-3-030-03766-6_46
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