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Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data
Motivation: Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typica...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2015
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4528629/ https://www.ncbi.nlm.nih.gov/pubmed/25861969 http://dx.doi.org/10.1093/bioinformatics/btv197 |