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High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. COCA improves upon PCA and sp...
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| Publié dans: | IEEE Trans Pattern Anal Mach Intell |
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| Auteurs principaux: | , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
2014
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5266498/ https://ncbi.nlm.nih.gov/pubmed/26352632 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TPAMI.2014.2307886 |
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