Functional Generalized Canonical Correlation Analysis for studying multiple longitudinal variables

1 code implementation • 11 Oct 2023 • Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus

In this paper, we introduce Functional Generalized Canonical Correlation Analysis (FGCCA), a new framework for exploring associations between multiple random processes observed jointly.

Tensor Generalized Canonical Correlation Analysis

1 code implementation • 10 Feb 2023 • Fabien Girka, Arnaud Gloaguen, Laurent Le Brusquet, Violetta Zujovic, Arthur Tenenhaus

Regularized Generalized Canonical Correlation Analysis (RGCCA) is a general statistical framework for multi-block data analysis.

Fast Non-Parametric Tests of Relative Dependency and Similarity

no code implementations • 17 Nov 2016 • Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blashcko

The second test, called the relative test of similarity, is use to determine which of the two samples from arbitrary distributions is significantly closer to a reference sample of interest and the relative measure of similarity is based on the Maximum Mean Discrepancy (MMD).

A general multiblock method for structured variable selection

no code implementations • 29 Oct 2016 • Tommy Löfstedt, Fouad Hadj-Selem, Vincent Guillemot, Cathy Philippe, Nicolas Raymond, Edouard Duchesney, Vincent Frouin, Arthur Tenenhaus

However, for technical reasons, the variable selection offered by SGCCA was restricted to a covariance link between the blocks (i. e., with $\tau=1$).

Variable Selection

A low variance consistent test of relative dependency

1 code implementation • 15 Jun 2014 • Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko

Such a test enables us to determine whether one source variable is significantly more dependent on a first target variable or a second.