Literature DB >> 28386813

Simultaneous Component Analysis by Means of Tucker3.

Alwin Stegeman1,2.   

Abstract

A new model for simultaneous component analysis (SCA) is introduced that contains the existing SCA models with common loading matrix as special cases. The new SCA-T3 model is a multi-set generalization of the Tucker3 model for component analysis of three-way data. For each mode (observational units, variables, sets) a different number of components can be chosen and the obtained solution can be rotated without loss of fit to facilitate interpretation. SCA-T3 can be fitted on centered multi-set data and also on the corresponding covariance matrices. For this purpose, alternating least squares algorithms are derived. SCA-T3 is evaluated in a simulation study, and its practical merits are demonstrated for several benchmark datasets.

Keywords:  multi-set data; parafac; rotation; simultaneous components analysis; tucker

Mesh:

Year:  2017        PMID: 28386813     DOI: 10.1007/s11336-017-9568-7

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  10 in total

1.  A clusterwise simultaneous component method for capturing within-cluster differences in component variances and correlations.

Authors:  Kim De Roover; Eva Ceulemans; Marieke E Timmerman; Patrick Onghena
Journal:  Br J Math Stat Psychol       Date:  2012-02-07       Impact factor: 3.380

2.  Clusterwise simultaneous component analysis for analyzing structural differences in multivariate multiblock data.

Authors:  Kim De Roover; Eva Ceulemans; Marieke E Timmerman; Kristof Vansteelandt; Jeroen Stouten; Patrick Onghena
Journal:  Psychol Methods       Date:  2011-10-03

3.  Multi-set factor analysis by means of Parafac2.

Authors:  Alwin Stegeman; Tam T T Lam
Journal:  Br J Math Stat Psychol       Date:  2015-11-03       Impact factor: 3.380

4.  Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method.

Authors:  Eva Ceulemans; Henk A L Kiers
Journal:  Br J Math Stat Psychol       Date:  2006-05       Impact factor: 3.380

5.  The special sign indeterminacy of the direct-fitting Parafac2 model: some implications, cautions, and recommendations for simultaneous component analysis.

Authors:  Nathaniel E Helwig
Journal:  Psychometrika       Date:  2013-02-27       Impact factor: 2.500

6.  Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis.

Authors:  Kim De Roover; Eva Ceulemans; Marieke E Timmerman; John B Nezlek; Patrick Onghena
Journal:  Psychometrika       Date:  2013-01-25       Impact factor: 2.500

7.  Three-mode factor analysis by means of Candecomp/Parafac.

Authors:  Alwin Stegeman; Tam T T Lam
Journal:  Psychometrika       Date:  2013-11-23       Impact factor: 2.500

8.  Structure and variation of mood in individuals with Parkinson's disease: a dynamic factor analysis.

Authors:  K Shifren; K Hooker; P Wood; J R Nesselroade
Journal:  Psychol Aging       Date:  1997-06

9.  Development and validation of brief measures of positive and negative affect: the PANAS scales.

Authors:  D Watson; L A Clark; A Tellegen
Journal:  J Pers Soc Psychol       Date:  1988-06

10.  Some mathematical notes on three-mode factor analysis.

Authors:  L R Tucker
Journal:  Psychometrika       Date:  1966-09       Impact factor: 2.500

  10 in total

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