4 citations · 11 across the 8 of their papers we have counts for
4 papers · 1 filter
Latent Functional PARAFAC for modeling multidimensional longitudinal data
Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus
In numerous settings, it is increasingly common to deal with longitudinal data organized as high-dimensional multi-dimensional arrays, also known as tensors. Within this framework,…
Functional Generalized Canonical Correlation Analysis for studying multiple longitudinal variables
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 joi…
Regularized Consensus PCA
Michel Tenenhaus, Arthur Tenenhaus, Patrick J. F. Groenen
A new framework for many multiblock component methods (including consensus and hierarchical PCA) is proposed. It is based on the consensus PCA model: a scheme connecting each block…
Jointly Sparse Global SIMPLS Regression
Tzu-Yu Liu, Laura Trinchera, Arthur Tenenhaus +2
Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. Since partial least squares regression (PLS-R) does not requi…