Literature DB >> 20084175

Degeneracy in Candecomp/Parafac and Indscal Explained For Several Three-Sliced Arrays With A Two-Valued Typical Rank.

Alwin Stegeman1.   

Abstract

The Candecomp/Parafac (CP) method decomposes a three-way array into a prespecified number R of rank-1 arrays, by minimizing the sum of squares of the residual array. The practical use of CP is sometimes complicated by the occurrence of so-called degenerate sequences of solutions, in which several rank-1 arrays become highly correlated in all three modes and some elements of the rank-1 arrays become arbitrarily large. We consider the real-valued CP decomposition of all known three-sliced arrays, i.e., of size pxqx3, with a two-valued typical rank. These are the 5x3x3 and 8x4x3 arrays, and the 3x3x4 and 3x3x5 arrays with symmetric 3x3 slices. In the latter two cases, CP is equivalent to the Indscal model. For a typical rank of {m,m+1}, we consider the CP decomposition with R=m of an array of rank m+1. We show that (in most cases) the CP objective function does not have a minimum but an infimum. Moreover, any sequence of feasible CP solutions in which the objective value approaches the infimum will become degenerate. We use the tools developed in Stegeman (2006), who considers pxpx2 arrays, and present a framework of analysis which is of use to the future study of CP degeneracy related to a two-valued typical rank. Moreover, our examples show that CP uniqueness is not necessary for degenerate solutions to occur.

Entities:  

Year:  2007        PMID: 20084175      PMCID: PMC2806219          DOI: 10.1007/s11336-007-9022-3

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


  1 in total

1.  A PARAFAC algorithm using penalty diagonalization error (PDE) for three-way data array resolution.

Authors:  Y Z Cao; Z P Chen; C Y Mo; H L Wu; R Q Yu
Journal:  Analyst       Date:  2000-12       Impact factor: 4.616

  1 in total
  6 in total

1.  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

2.  Constrained Candecomp/Parafac via the Lasso.

Authors:  Paolo Giordani; Roberto Rocci
Journal:  Psychometrika       Date:  2013-02-07       Impact factor: 2.500

3.  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

4.  Exploring individual and group differences in latent brain networks using cross-validated simultaneous component analysis.

Authors:  Nathaniel E Helwig; Matthew A Snodgress
Journal:  Neuroimage       Date:  2019-07-15       Impact factor: 6.556

5.  On the Non-Existence of Optimal Solutions and the Occurrence of "Degeneracy" in the CANDECOMP/PARAFAC Model.

Authors:  Wim P Krijnen; Theo K Dijkstra; Alwin Stegeman
Journal:  Psychometrika       Date:  2008-01-29       Impact factor: 2.500

6.  Exploring dynamic metabolomics data with multiway data analysis: a simulation study.

Authors:  Lu Li; Huub Hoefsloot; Albert A de Graaf; Evrim Acar; Age K Smilde
Journal:  BMC Bioinformatics       Date:  2022-01-10       Impact factor: 3.169

  6 in total

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