Abstract
High-dimensional data presents a big challenge for the clustering problem, however, the high-dimensional data often lie in low-dimensional subspaces. So, subspace clustering has been widely researched. Sparse subspace clustering (SSC) is considered as the state-of-the-art method for subspace clustering, it has received an increasing amount of interest in recent years. In this paper, we propose a novel sparse subspace clustering method named graph regularized structured sparse subspace clustering (GS3C) to jointly analyze the data under a single clustering framework and with shared underlying sparse representations. Two convex regularizations are combined and used in the model to enable sparsity as well as to facilitate multi-task learning. We also introduced the graph regularization to improve stability and consistency. The effectiveness of the proposed algorithm is demonstrated through experiments on motion segmentation and face clustering.
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Acknowledgements
This work is supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20130093110009), the National Natural Science Foundation of China (Grant No. 61373055) and the Research Project on Surveying and Mapping of Jiangsu Province (Grant No. JSCHKY201109).
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You, CZ., Wu, XJ. (2015). Graph Regularized Structured Sparse Subspace Clustering. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_14
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DOI: https://doi.org/10.1007/978-3-319-23989-7_14
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