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Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data
We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high dimensional non-Gaussian data. Compared with sparse PCA, our method has weaker modeling assumption and is more robust to possible data contamination. Theoretically, the proposed method...
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| Main Authors: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2014
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4051512/ https://ncbi.nlm.nih.gov/pubmed/24932056 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2013.844699 |
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