<abstract xmlns="http://www.w3.org/1999/xhtml"><p>There is a growing need to analyze data sets characterized by several sets of variables observed on the same set of individuals. Such complex data structures are known as multiblock (or multiple-set) data sets. Multi-block data sets are encountered in diverse fields including bioinformatics, chemometrics, food analysis, etc. Generalized Canonical Correlation Analysis (GCCA) is a very powerful method to study this kind of relationships between blocks. It can also be viewed as a method for the integration of information from <italic>K ></italic> 2 distinct sources (Takane and Oshima-Takane 2002). In this paper, GCCA is considered in the context of multivariate functional data. Such data are treated as realizations of multivariate random processes. GCCA is a technique that allows the joint analysis of several sets of data through dimensionality reduction. The central problem of GCCA is to construct a series of components aiming to maximize the association among the multiple variable sets. This method will be presented for multivariate functional data. Finally, a practical example will be discussed.</p></abstract>

There is a growing need to analyze data sets characterized by several sets of variables observed on the same set of individuals. Such complex data structures are known as multiblock (or multiple-set) data sets. Multi-block data sets are encountered in diverse fields including bioinformatics, chemometrics, food analysis, etc. Generalized Canonical Correlation Analysis (GCCA) is a very powerful method to study this kind of relationships between blocks. It can also be viewed as a method for the integration of information from K > 2 distinct sources (Takane and Oshima-Takane 2002). In this paper, GCCA is considered in the context of multivariate functional data. Such data are treated as realizations of multivariate random processes. GCCA is a technique that allows the joint analysis of several sets of data through dimensionality reduction. The central problem of GCCA is to construct a series of components aiming to maximize the association among the multiple variable sets. This method will be presented for multivariate functional data. Finally, a practical example will be discussed.

Generalized canonical correlation analysis for functional data

Faculty of Mathematics and Computer Science

Adam Mickiewicz University, Uniwersytetu Poznańskiego 4

The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Generalized canonical correlation analysis for functional data"}

There is a growing need to analyze data sets characterized by several sets of variables observed on the same set of individuals. Such complex data structures...

Generalized canonical correlation analysis for functional data

Publicado en línea: 12 jun 2020

Páginas: 1 - 12

DOI: https://doi.org/10.2478/bile-2020-0001

Palabras clave
multivariate functional data, generalized canonical correlation analysis

© 2020 Tomasz Górecki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Generalized canonical correlation analysis for functional data

Publicado en línea: 12 jun 2020

Páginas: 1 - 12

DOI: https://doi.org/10.2478/bile-2020-0001

Palabras clavemultivariate functional data, generalized canonical correlation analysis

© 2020 Tomasz Górecki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
multivariate functional data, generalized canonical correlation analysis