Cargando…
Stepwise Covariance-Free Common Principal Components (CF-CPC) With an Application to Neuroscience
Finding the common principal component (CPC) for ultra-high dimensional data is a multivariate technique used to discover the latent structure of covariance matrices of shared variables measured in two or more k conditions. Common eigenvectors are assumed for the covariance matrix of all conditions,...
Autores principales: | Riaz, Usama, Razzaq, Fuleah A., Hu, Shiang, Valdés-Sosa, Pedro A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636064/ https://www.ncbi.nlm.nih.gov/pubmed/34867161 http://dx.doi.org/10.3389/fnins.2021.750290 |
Ejemplares similares
-
Causal effects of cingulate morphology on executive functions in healthy young adults
por: Razzaq, Fuleah A., et al.
Publicado: (2022) -
Covariate-Adjusted Hybrid Principal Components Analysis
por: Scheffler, Aaron Wolfe, et al.
Publicado: (2020) -
Thermo diffusion impacts on the flow of second grade fluid with application of (ABC), (CF) and (CPC) subject to exponential heating
por: Rehman, Aziz Ur, et al.
Publicado: (2022) -
Reconstruction of comminuted long-bone fracture using CF/CPC scaffolds manufactured by rapid prototyping
por: Huang, Sheng-Li, et al.
Publicado: (2012) -
Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices
por: Meyer, Karin, et al.
Publicado: (2005)