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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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Frontiers Media S.A.
2021
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| Accès en ligne: | https://doaj.org/article/bdc035ecc71047b39a5c6301c4f74fc2 |
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