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...

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Detalles Bibliográficos
Autores principales: Sill, Martin, Saadati, Maral, Benner, Axel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Original Papers
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

Internet

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

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