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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods.

Michel Tenenhaus1, Arthur Tenenhaus2,3, Patrick J F Groenen4

  • 1HEC Paris, Jouy-en-Josas, France.

Psychometrika
|May 25, 2017
PubMed
Summary
This summary is machine-generated.

A new framework for sequential multiblock component analysis using regularized generalized canonical correlation analysis (RGCCA) was developed. This method offers a convergent algorithm, recovering established techniques and providing a stationary point for RGCCA.

Keywords:
GCCAMAXBETMAXDIFFMAXVARPLS path modelingRGCCASSQCORSUMCORconsensus PCAhierarchical PCAmultiblock component methods

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Area of Science:

  • Multivariate statistics
  • Chemometrics
  • Machine learning

Background:

  • Sequential multiblock component methods are crucial for analyzing complex datasets.
  • Existing methods may lack flexibility in handling block interdependencies.
  • Regularized Generalized Canonical Correlation Analysis (RGCCA) offers a powerful framework for such analyses.

Purpose of the Study:

  • To introduce a novel framework for sequential multiblock component methods.
  • To extend RGCCA by incorporating diverse scheme functions and shrinkage constants.
  • To analyze different block connection strategies, including full and superblock connections.

Main Methods:

  • Development of a new iterative algorithm for RGCCA.
  • Consideration of various scheme functions (e.g., linear, polynomial) and shrinkage constants (0 or 1).
  • Investigation of two block connection types: fully connected and superblock connected.

Main Results:

  • The proposed algorithm guarantees monotone convergence to a stationary point of RGCCA.
  • In specific scenarios, the RGCCA solution corresponds to the leading eigenvalue/eigenvector of a derived matrix.
  • The framework unifies numerous existing multiblock component methods under specific parameter choices.

Conclusions:

  • The new RGCCA framework provides a robust and unified approach to sequential multiblock component analysis.
  • The convergent algorithm ensures reliable solutions.
  • This method enhances the flexibility and applicability of multiblock component techniques.