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A Nonconvex Implementation of Sparse Subspace Clustering: Algorithm and Convergence Analysis
2020
IEEE Access
Subspace clustering has been widely applied to detect meaningful clusters in high-dimensional data spaces. And the sparse subspace clustering (SSC) obtains superior clustering performance by solving a relaxed 0 -minimization problem with 1 -norm. Although the use of 1 -norm instead of the 0 one can make the object function convex, it causes large errors on large coefficients in some cases. In this paper, we study the sparse subspace clustering algorithm based on a nonconvex modeling
doi:10.1109/access.2020.2981740
fatcat:6rpxuu275fgabjg2ge6rln4k5m