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- W2293935805 abstract "A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The corresponding subspace clustering problem has been extensively studied in the literature, partitioning such high-dimensional data into clusters corresponding to their underlying low-dimensional subspaces. However, low-dimensional intrinsic structures are often violated for real-world observations, as they can be corrupted by errors or deviate from ideal models. We propose to address this by learning a linear transformation on subspaces using matrix rank, via its convex surrogate nuclear norm, as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the same subspace, and, at the same time, forces a high-rank structure for data from different subspaces. In this way, we reduce variations within the subspaces, and increase separation between the subspaces for improved subspace clustering and classification." @default.
- W2293935805 created "2016-06-24" @default.
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- W2293935805 date "1985-01-01" @default.
- W2293935805 modified "2023-09-23" @default.
- W2293935805 title "Learning Transformations" @default.
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- W2293935805 doi "https://doi.org/10.7551/mitpress/1074.003.0007" @default.
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