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,...

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Detalles Bibliográficos
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:
Neuroscience
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

Internet

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

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