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- W3100128943 abstract "Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering data points into their underlying subspaces. However, SSC may suffer from heavy computational burden. Orthogonal Matching Pursuit applied on SSC accelerates the computation but the trade-off is the loss of clustering accuracy. In this paper, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and maintain the low computational time by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SCC. And the framework is scalable for other data point selection strategies. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared with other clustering methods in terms of accuracy and computational time." @default.
- W3100128943 created "2020-11-23" @default.
- W3100128943 creator A5026086282 @default.
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- W3100128943 date "2019-12-01" @default.
- W3100128943 modified "2023-09-28" @default.
- W3100128943 title "Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering" @default.
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- W3100128943 doi "https://doi.org/10.1109/lsp.2019.2953638" @default.
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