Cargando…
A Guide for Sparse PCA: Model Comparison and Applications
PCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify th...
Autores principales: | Guerra-Urzola, Rosember, Van Deun, Katrijn, Vera, Juan C., Sijtsma, Klaas |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636462/ https://www.ncbi.nlm.nih.gov/pubmed/34185214 http://dx.doi.org/10.1007/s11336-021-09773-2 |
Ejemplares similares
-
Obtaining insights from high-dimensional data: sparse principal covariates regression
por: Van Deun, Katrijn, et al.
Publicado: (2018) -
A flexible framework for sparse simultaneous component based data integration
por: Van Deun, Katrijn, et al.
Publicado: (2011) -
MOSS: multi-omic integration with sparse value decomposition
por: Gonzalez-Reymundez, Agustin, et al.
Publicado: (2022) -
Sparse Project VCF: efficient encoding of population genotype matrices
por: Lin, Michael F, et al.
Publicado: (2020) -
MBG: Minimizer-based sparse de Bruijn Graph construction
por: Rautiainen, Mikko, et al.
Publicado: (2021)