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• W2532937011 abstract "The covariance descriptor which is a symmetric positive definite (SPD) matrix, has recently attracted considerable attentions in computer vision. However, it is not trivial issue to handle its non-linearity in semi-supervised learning. To this end, in this paper, a semi-supervised sparse subspace clustering on SPD manifolds is proposed, via considering the intrinsic geometric structure within the manifold-valued data. Experimental results on two databases show that our method can provide better clustering solutions than the state-of-the-art approaches thanks to incorporating Riemannian geometry structure." @default.
• W2532937011 created "2016-10-28" @default.
• W2532937011 creator A5043262479 @default.
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• W2532937011 date "2016-01-01" @default.
• W2532937011 modified "2023-09-27" @default.
• W2532937011 title "Semi-supervised Sparse Subspace Clustering on Symmetric Positive Definite Manifolds" @default.
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• W2532937011 doi "https://doi.org/10.1007/978-981-10-3002-4_49" @default.
• W2532937011 hasPublicationYear "2016" @default.
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