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Deng et al., 2013 - Google Patents

Low-rank structure learning via nonconvex heuristic recovery

Deng et al., 2013

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Document ID
16334151461575329266
Author
Deng Y
Dai Q
Liu R
Zhang Z
Hu S
Publication year
Publication venue
IEEE transactions on neural networks and learning systems

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Snippet

In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex …
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    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
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