Regularized Generalized Canonical Correlation Analysis

By Arthur Tenenhaus, Michel Tenenhaus. European Journal of Operational Research, 2011

379 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Food Science

View Paper: https://www.semanticscholar.org/paper/70b0790e1b9f253159486d1bf149bfeed2bb5a9c

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Citations in the corpus

Non-Metric Partial Least Squares (2012, methodology): “Tenenhaus and Tenenhaus [31] also demonstrated that new Mode A PLS-PM, when centroid or factorial schemes are used, monotonically converges, for each Table 1 Iterative steps of the NIPALS algorithm, PLS-R algorithm (ﬁrst component) and PLS-PM algorithm with new Mode A option.”
Harnessing the complexity of metabolomic data with chemometrics (2014, methodology): “sion and its extensions [42,47], PLS path modelling [48], generalised canonical analysis [49] and multiblock redundancy”
Common Beliefs and Reality About PLS (2014, background): “Indeed, there has been substantial further development of PLS—including by Dijkstra himself 1 — and there is a large body of literature on PLS published in other methods and statistics journals than the ones considered by R&E, including Psychometrika (A. Tenenhaus & Tenenhaus, 2011; Dijkstra & Scher”
On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, methodology): “Researchers who intend to estimate a factor model should use CB-SEM, while researchers intending to estimate a model of composites should use a composite-based method like PLS-SEM, GSCA, or another in that class (e.g., Dijkstra & Henseler, 2011; Tenenhaus & Tenenhaus, 2011).”
DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays (2019, methodology): “…Explained (JIVE) (Lock et al. , 2013), sparse MultiBlock Partial Least Squares (Li et al. , 2012), regularized and sparse generalized canonical correlation analysis (sGCCA) (Tenenhaus and Tenenhaus, 2011; Tenenhaus et al. , 2014) and Multi-Omics Factor Analysis (MOFA) (Argelaguet et al. , 2018).”
Quadratic Shrinkage for Large Covariance Matrices (2020, background): “, 2009), psychology (Markon, 2010), political science (Tenenhaus and Tenenhaus, 2011), analytical chemistry (Guo et al.”
Using PLS path modeling in new technology research: updated guidelines (2016, methodology): “…1 by alternative indicator weight generators, such as principal component analysis (Tenenhaus, 2008), generalized structured component analysis (Hwang and Takane, 2004; Henseler, 2012), regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus, 2011), or even plain sum scores.”
Goodness-of-fit indices for partial least squares path modeling (2013, methodology): “A small modification of the PLS algorithm is needed to actually maximize a covariance criterion, but simulations show that both approaches are in very close correspondence (Tenenhaus and Tenenhaus 2011).”
A new criterion for assessing discriminant validity in variance-based structural equation modeling (2015, methodology): “Variance-based SEM methods—such as partial least squares path modeling (PLS; Lohmöller 1989; Wold 1982), generalized structured component analysis (GSCA; Henseler 2012; Hwang and Takane 2004), regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus 2011), and best fitting pro”
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis (2019, background): “• GCCA: Generalized CCA (Tenenhaus and Tenenhaus 2011) learns a common subspace across multiple views.”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “Future research should also include a broader set of composite-based SEM methods such as regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus 2011), best fitting proper indices (Dijkstra and Henseler 2011), and the extended PLS algorithm, which Lohmöller (1989)…”
Linking Image and Text with 2-Way Nets (2016, methodology): “The algorithm has been generalized in many ways: regularization was added [29], kernels were introduced [2, 30, 4], versions for more than two sources were developed [41] etc.”
Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling (2017, methodology): “…generalized structured component analysis (Hwang and Takane 2004), traditional partial least squares (PLS) path modeling (Lohm€oller 1989), consistent PLS path modeling (Dijkstra and Henseler 2015a, 2015b), and regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus 2011).”
On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012) (2014, background): “Whereas in CB-SEM, fit refers to the distance between an observed covariance matrix and an implied covariance matrix, in PLS-SEM fit relates to the degree towhich a correlation or covariancebased criterion is being maximized (Hanafi, 2007; Tenenhaus and Tenenhaus, 2011).”