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Robust Generalized Low Rank Approximations of Matrices
In recent years, the intrinsic low rank structure of some datasets has been extensively exploited to reduce dimensionality, remove noise and complete the missing entries. As a well-known technique for dimensionality reduction and data compression, Generalized Low Rank Approximations of Matrices (GLR...
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| Pubblicato in: | PLoS One |
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| Autori principali: | , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Public Library of Science
2015
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4569376/ https://ncbi.nlm.nih.gov/pubmed/26367116 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0138028 |
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