IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v75y2014icp203-216.html
   My bibliography  Save this article

Finding the limit of diverging components in three-way Candecomp/Parafac—A demonstration of its practical merits

Author

Listed:
  • Stegeman, Alwin

Abstract

Three-way Candecomp/Parafac (CP) is a three-way generalization of principal component analysis (PCA) for matrices. Contrary to PCA, a CP decomposition is rotationally unique under mild conditions. However, a CP analysis may be hampered by the non-existence of a best-fitting CP decomposition with R≥2 components. In this case, fitting CP to a three-way data array results in diverging CP components. Recently, it has been shown that this can be solved by fitting a decomposition with several interaction terms, using initial values obtained from the diverging CP decomposition. The new decomposition is called CPlimit, since it is the limit of the diverging CP decomposition. The practical merits of this procedure are demonstrated for a well-known three-way dataset of TV-ratings. CPlimit finds main components with the same interpretation as Tucker models or when imposing orthogonality in CP. However, CPlimit has higher joint fit of the main components than Tucker models, contains only one small interaction term, and does not impose the unnatural constraint of orthogonality. The uniqueness properties of the CPlimit decomposition are discussed in detail.

Suggested Citation

  • Stegeman, Alwin, 2014. "Finding the limit of diverging components in three-way Candecomp/Parafac—A demonstration of its practical merits," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 203-216.
  • Handle: RePEc:eee:csdana:v:75:y:2014:i:c:p:203-216
    DOI: 10.1016/j.csda.2014.02.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947314000504
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2014.02.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kiers, Henk A. L., 1998. "Three-way SIMPLIMAX for oblique rotation of the three-mode factor analysis core to simple structure," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 307-324, September.
    2. Pieter Kroonenberg & Jan Leeuw, 1980. "Principal component analysis of three-mode data by means of alternating least squares algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(1), pages 69-97, March.
    3. Wim Krijnen & Theo Dijkstra & Alwin Stegeman, 2008. "On the Non-Existence of Optimal Solutions and the Occurrence of “Degeneracy” in the CANDECOMP/PARAFAC Model," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 431-439, September.
    4. Siciliano, Roberta & Mooijaart, Ab, 1997. "Three-factor association models for three-way contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 24(3), pages 337-356, May.
    5. Henk Kiers, 2006. "Properties of and Algorithms for Fitting Three-Way Component Models with Offset Terms," Psychometrika, Springer;The Psychometric Society, vol. 71(2), pages 231-256, June.
    6. J. Carroll & Jih-Jie Chang, 1970. "Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 283-319, September.
    7. Alwin Stegeman, 2006. "Degeneracy in Candecomp/Parafac explained for p × p × 2 arrays of rank p + 1 or higher," Psychometrika, Springer;The Psychometric Society, vol. 71(3), pages 483-501, September.
    8. Alwin Stegeman, 2007. "Degeneracy in Candecomp/Parafac and Indscal Explained For Several Three-Sliced Arrays With A Two-Valued Typical Rank," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 601-619, December.
    9. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
    10. Harshman, Richard A. & Lundy, Margaret E., 1994. "PARAFAC: Parallel factor analysis," Computational Statistics & Data Analysis, Elsevier, vol. 18(1), pages 39-72, August.
    11. Tomasi, Giorgio & Bro, Rasmus, 2006. "A comparison of algorithms for fitting the PARAFAC model," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1700-1734, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alwin Stegeman, 2018. "Simultaneous Component Analysis by Means of Tucker3," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 21-47, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alwin Stegeman & Tam Lam, 2014. "Three-Mode Factor Analysis by Means of Candecomp/Parafac," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 426-443, July.
    2. Paolo Giordani & Roberto Rocci, 2013. "Constrained Candecomp/Parafac via the Lasso," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 669-684, October.
    3. Dawn Iacobucci & Doug Grisaffe & Wayne DeSarbo, 2017. "Statistical perceptual maps: using confidence region ellipses to enhance the interpretations of brand positions in multidimensional scaling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 5(3), pages 81-98, December.
    4. Alwin Stegeman, 2018. "Simultaneous Component Analysis by Means of Tucker3," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 21-47, March.
    5. Paolo Giordani & Roberto Rocci & Giuseppe Bove, 2020. "Factor Uniqueness of the Structural Parafac Model," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 555-574, September.
    6. Roberto Rocci & Jos Berge, 2002. "Transforming three-way arrays to maximal simplicity," Psychometrika, Springer;The Psychometric Society, vol. 67(3), pages 351-365, September.
    7. Richard Harshman & Margaret Lundy, 1996. "Uniqueness proof for a family of models sharing features of Tucker's three-mode factor analysis and PARAFAC/candecomp," Psychometrika, Springer;The Psychometric Society, vol. 61(1), pages 133-154, March.
    8. Nathaniel Helwig, 2013. "The Special Sign Indeterminacy of the Direct-Fitting Parafac2 Model: Some Implications, Cautions, and Recommendations for Simultaneous Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 725-739, October.
    9. Mariela González-Narváez & María José Fernández-Gómez & Susana Mendes & José-Luis Molina & Omar Ruiz-Barzola & Purificación Galindo-Villardón, 2021. "Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    10. Henk Kiers, 1991. "Hierarchical relations among three-way methods," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 449-470, September.
    11. Willem Kloot & Pieter Kroonenberg, 1985. "External analysis with three-mode principal component models," Psychometrika, Springer;The Psychometric Society, vol. 50(4), pages 479-494, December.
    12. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    13. Giuseppe Brandi & Ruggero Gramatica & Tiziana Di Matteo, 2019. "Unveil stock correlation via a new tensor-based decomposition method," Papers 1911.06126, arXiv.org, revised Apr 2020.
    14. Wilderjans, Tom & Ceulemans, Eva & Van Mechelen, Iven, 2009. "Simultaneous analysis of coupled data blocks differing in size: A comparison of two weighting schemes," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1086-1098, February.
    15. Kohei Adachi, 2009. "Joint Procrustes Analysis for Simultaneous Nonsingular Transformation of Component Score and Loading Matrices," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 667-683, December.
    16. Michel Velden & Tammo Bijmolt, 2006. "Generalized canonical correlation analysis of matrices with missing rows: a simulation study," Psychometrika, Springer;The Psychometric Society, vol. 71(2), pages 323-331, June.
    17. Bilian Chen & Zhening Li & Shuzhong Zhang, 2015. "On optimal low rank Tucker approximation for tensors: the case for an adjustable core size," Journal of Global Optimization, Springer, vol. 62(4), pages 811-832, August.
    18. Timmerman, Marieke E. & Kiers, Henk A. L., 2002. "Three-way component analysis with smoothness constraints," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 447-470, September.
    19. Veldscholte, Carla M. & Kroonenberg, Pieter M. & Antonides, Gerrit, 1998. "Three-mode analysis of perceptions of economic activities in Eastern and Western Europe1," Journal of Economic Psychology, Elsevier, vol. 19(3), pages 321-351, June.
    20. Rubinstein, Alexander & Slutskin, Lev, 2018. "«Multiway data analysis» and the general problem of journals’ ranking," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 90-113.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:75:y:2014:i:c:p:203-216. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.