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On Consistency and Sparsity for Principal Components Analysis in High Dimensions
Principal components analysis (PCA) is a classic method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. Contemporary datasets often have p comparable with or even much larger than n. Our main assertions, in such settings, are (a) tha...
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| Main Authors: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2009
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2898454/ https://ncbi.nlm.nih.gov/pubmed/20617121 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1198/jasa.2009.0121 |
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