Multiway generalized canonical correlation analysis.
Biostatistics
; 23(1): 240-256, 2022 01 13.
Article
in En
| MEDLINE
| ID: mdl-32451525
ABSTRACT
Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied, and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using electroencephalography (EEG).
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electroencephalography
/
Canonical Correlation Analysis
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Biostatistics
Year:
2022
Document type:
Article
Affiliation country: