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• W79405465 abstract "We propose low-rank representation (LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowest-rank representation among all the candidates that represent all vectors as the linear combination of the bases in a dictionary. Unlike the well-known sparse representation (SR), which computes the sparsest representation of each data vector individually, LRR aims at finding the lowest-rank representation of a collection of vectors jointly. LRR better captures the global structure of data, giving a more effective tool for robust subspace segmentation from corrupted data. Both theoretical and experimental results show that LRR is a promising tool for subspace segmentation." @default.
• W79405465 created "2016-06-24" @default.
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• W79405465 date "2010-06-21" @default.
• W79405465 modified "2023-10-10" @default.
• W79405465 title "Robust Subspace Segmentation by Low-Rank Representation" @default.
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