IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i1p143-153.html
   My bibliography  Save this article

Acceleration of the alternating least squares algorithm for principal components analysis

Author

Listed:
  • Kuroda, Masahiro
  • Mori, Yuichi
  • Iizuka, Masaya
  • Sakakihara, Michio

Abstract

Principal components analysis (PCA) is a popular descriptive multivariate method for handling quantitative data and it can be extended to deal with qualitative data and mixed measurement level data. The existing algorithms for extended PCA are PRINCIPALS of Young et al. (1978) and PRINCALS of Gifi (1989) in which the alternating least squares algorithm is utilized. These algorithms based on the least squares estimation may require many iterations in their application to very large data sets and variable selection problems and may take a long time to converge. In this paper, we derive a new iterative algorithm for accelerating the convergence of PRINCIPALS and PRINCALS by using the vector [epsilon] algorithm of Wynn (1962). The proposed acceleration algorithm speeds up the convergence of the sequence of the parameter estimates obtained from PRINCIPALS or PRINCALS. Numerical experiments illustrate the potential of the proposed acceleration algorithm.

Suggested Citation

  • Kuroda, Masahiro & Mori, Yuichi & Iizuka, Masaya & Sakakihara, Michio, 2011. "Acceleration of the alternating least squares algorithm for principal components analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 143-153, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:143-153
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00245-8
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Mingfeng Wang & Masahiro Kuroda & Michio Sakakihara & Zhi Geng, 2008. "Acceleration of the EM algorithm using the vector epsilon algorithm," Computational Statistics, Springer, vol. 23(3), pages 469-486, July.
    2. Forrest Young & Yoshio Takane & Jan Leeuw, 1978. "The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 43(2), pages 279-281, June.
    3. Forrest Young, 1981. "Quantitative analysis of qualitative data," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 357-388, December.
    4. Kuroda, Masahiro & Sakakihara, Michio, 2006. "Accelerating the convergence of the EM algorithm using the vector [epsilon] algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1549-1561, December.
    5. Krijnen, Wim P., 2006. "Convergence of the sequence of parameters generated by alternating least squares algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 481-489, November.
    6. Kiers, Henk A. L., 2002. "Setting up alternating least squares and iterative majorization algorithms for solving various matrix optimization problems," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 157-170, November.
    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. Masahiro Kuroda & Yuichi Mori & Masaya Iizuka, 2023. "Speeding up the convergence of the alternating least squares algorithm using vector $$\varepsilon $$ ε acceleration and restarting for nonlinear principal component analysis," Computational Statistics, Springer, vol. 38(1), pages 243-262, 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. Tian, Guo-Liang & Tang, Man-Lai & Liu, Chunling, 2012. "Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 255-265.
    2. van Deun, K. & Groenen, P.J.F. & Delbeke, L., 2005. "VIPSCAL: A combined vector ideal point model for preference data," Econometric Institute Research Papers EI 2005-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Groenen, P.J.F. & Nalbantov, G.I. & Bioch, J.C., 2007. "SVM-Maj: a majorization approach to linear support vector machines with different hinge errors," Econometric Institute Research Papers EI 2007-49, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Hansohm, Jürgen, 2004. "Algorithmus und Programm zur Bestimmung der monotonen Kleinst-Quadrate Lösung bei partiellen Präordnungen," Arbeitspapiere zur mathematischen Wirtschaftsforschung 187, Universität Augsburg, Institut für Statistik und Mathematische Wirtschaftstheorie.
    5. Berlinet, A.F. & Roland, Ch., 2012. "Acceleration of the EM algorithm: P-EM versus epsilon algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4122-4137.
    6. Yoo, Boonghee, 2009. "Developing an overall ranking of 79 marketing journals: An introduction of PRINQUAL to marketing," Australasian marketing journal, Elsevier, vol. 17(3), pages 160-174.
    7. Natalia Porto & Victoria Dowbley & Carolina Inés García, 2020. "Estrategias de turismo en la Provincia de Buenos Aires. Una clasificación regional utilizando análisis multivariado," CEFIP, Working Papers 035, CEFIP, Universidad Nacional de La Plata.
    8. Markovsky, Ivan & Niranjan, Mahesan, 2010. "Approximate low-rank factorization with structured factors," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3411-3420, December.
    9. Hansohm, Jürgen, 2007. "Algorithms and error estimations for monotone regression on partially preordered sets," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1043-1050, May.
    10. Masahiro Kuroda & Zhi Geng & Michio Sakakihara, 2015. "Improving the vector $$\varepsilon $$ ε acceleration for the EM algorithm using a re-starting procedure," Computational Statistics, Springer, vol. 30(4), pages 1051-1077, December.
    11. Masahiro Kuroda & Yuichi Mori & Masaya Iizuka, 2023. "Speeding up the convergence of the alternating least squares algorithm using vector $$\varepsilon $$ ε acceleration and restarting for nonlinear principal component analysis," Computational Statistics, Springer, vol. 38(1), pages 243-262, March.
    12. Katherine Morales & Miguel Flores & Yasmín Salazar Méndez, 2021. "Analysis of Principal Nonlinear Components for the Construction of a Socioeconomic Stratification Index in Ecuador," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, vol. 88(2), pages 43-82, July.
    13. Eeke Burg & Jan Leeuw & Renée Verdegaal, 1988. "Homogeneity analysis withk sets of variables: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 53(2), pages 177-197, June.
    14. Michel Tenenhaus, 1988. "Canonical analysis of two convex polyhedral cones and applications," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 503-524, December.
    15. Saâdaoui, Foued, 2023. "Randomized extrapolation for accelerating EM-type fixed-point algorithms," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    16. Naomichi Makino, 2015. "Generalized data-fitting factor analysis with multiple quantification of categorical variables," Computational Statistics, Springer, vol. 30(1), pages 279-292, March.
    17. van Rosmalen, J.M. & Koning, A.J. & Groenen, P.J.F., 2007. "Optimal Scaling of Interaction Effects in Generalized Linear Models," Econometric Institute Research Papers EI 2007-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Krijnen, Wim P., 2006. "Convergence of the sequence of parameters generated by alternating least squares algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 481-489, November.
    19. S. Winsberg & J. Ramsay, 1983. "Monotone spline transformations for dimension reduction," Psychometrika, Springer;The Psychometric Society, vol. 48(4), pages 575-595, December.
    20. Florian Schuberth & Jörg Henseler & Theo K. Dijkstra, 2018. "Partial least squares path modeling using ordinal categorical indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 9-35, January.

    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:55:y:2011:i:1:p:143-153. 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.