Translator Disclaimer
3 January 2020 Laplacian embedded sparse subspace clustering
Author Affiliations +
Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 1137318 (2020)
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China
Subspace clustering, which aims at yielding a low-dimensional structure of high-dimensional data, is a fundamental clustering problem. Sparse subspace clustering (SSC) achieves state-of-the-art clustering performances by imposing sparse constraint on the coefficient matrix. However, most SSCs do not exploit the intrinsic relationship or prior information embedded in data. In this paper, we propose Laplacian embedded sparse subspace clustering, in which the intrinsic relationship of data is enforced by introducing a graph Laplacian regularization term into the clustering model. Then a symmetric constraint is imposed on the sparse representation to guarantee weight consistency for each pair of data points. To further offset the instability and control smoothness, a consistency penalty term is utilized to encourage the sequential property of data. Finally, the inexact augmented Lagrange multipliers (ALM) technique is adopted to solve the optimization problem. Experimental results on real-world data sets demonstrate the superior performance of the proposed algorithm over state-of-the-art methods.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bing Yang and Zexuan Ji "Laplacian embedded sparse subspace clustering", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137318 (3 January 2020);

Back to Top