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Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

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  • Michael Brusco
  • Renu Singh
  • Douglas Steinley
Abstract
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Suggested Citation

  • Michael Brusco & Renu Singh & Douglas Steinley, 2009. "Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 705-726, December.
    • Handle: RePEc:spr:psycho:v:74:y:2009:i:4:p:705-726
    DOI: 10.1007/s11336-009-9130-3
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    References listed on IDEAS

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    1. P. Bentler, 1977. "Factor simplicity index and transformations," Psychometrika, Springer;The Psychometric Society, vol. 42(2), pages 277-295, June.
    2. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    3. W. J. Krzanowski, 1987. "Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(1), pages 22-33, March.
    4. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    5. Manuel Laguna & Fred Glover, 1993. "Bandwidth Packing: A Tabu Search Approach," Management Science, INFORMS, vol. 39(4), pages 492-500, April.
    6. Michael Brusco, 2002. "A branch-and-bound algorithm for fitting anti-robinson structures to symmetric dissimilarity matrices," Psychometrika, Springer;The Psychometric Society, vol. 67(3), pages 459-471, September.
    7. Lejeune, M.A., 2006. "A variable neighborhood decomposition search method for supply chain management planning problems," European Journal of Operational Research, Elsevier, vol. 175(2), pages 959-976, December.
    8. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    9. Douglas Steinley & Michael Brusco, 2008. "Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 125-144, March.
    10. J. Ramsay & Jos Berge & G. Styan, 1984. "Matrix correlation," Psychometrika, Springer;The Psychometric Society, vol. 49(3), pages 403-423, September.
    11. Kristine Hogarty & Jeffrey Kromrey & John Ferron & Constance Hines, 2004. "Selection of variables in exploratory factor analysis: An empirical comparison of a stepwise and traditional approach," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 593-611, December.
    12. I. T. Jolliffe, 1973. "Discarding Variables in a Principal Component Analysis. Ii: Real Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 21-31, March.
    13. Burke, Edmund K. & Curtois, Timothy & Post, Gerhard & Qu, Rong & Veltman, Bart, 2008. "A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem," European Journal of Operational Research, Elsevier, vol. 188(2), pages 330-341, July.
    14. Michael Brusco & J. Cradit, 2001. "A variable-selection heuristic for K-means clustering," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 249-270, June.
    15. A. M. Geoffrion & R. E. Marsten, 1972. "Integer Programming Algorithms: A Framework and State-of-the-Art Survey," Management Science, INFORMS, vol. 18(9), pages 465-491, May.
    16. Michael Brusco & Stephanie Stahl, 2005. "Optimal Least-Squares Unidimensional Scaling: Improved Branch-and-Bound Procedures and Comparison to Dynamic Programming," Psychometrika, Springer;The Psychometric Society, vol. 70(2), pages 253-270, June.
    17. P. Robert & Y. Escoufier, 1976. "A Unifying Tool for Linear Multivariate Statistical Methods: The RV‐Coefficient," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(3), pages 257-265, November.
    18. A. Pedro Duarte Silva, 2000. "DISCARDING VARIABLES in PRINCIPAL COMPONENT ANALYSIS : ALGORITHMS for ALL-SUBSETS COMPARISONS," Working Papers de Economia (Economics Working Papers) 02, Católica Porto Business School, Universidade Católica Portuguesa.
    19. Derpich, Ivan & Vera, Jorge R., 2006. "Improving the efficiency of the Branch and Bound algorithm for integer programming based on "flatness" information," European Journal of Operational Research, Elsevier, vol. 174(1), pages 92-101, October.
    20. Dray, Stephane, 2008. "On the number of principal components: A test of dimensionality based on measurements of similarity between matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2228-2237, January.
    21. Egon Balas, 1965. "An Additive Algorithm for Solving Linear Programs with Zero-One Variables," Operations Research, INFORMS, vol. 13(4), pages 517-546, August.
    22. Duarte Silva, António Pedro, 2001. "Efficient Variable Screening for Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 76(1), pages 35-62, January.
    23. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    24. Michael Brusco, 2006. "A Repetitive Branch-and-Bound Procedure for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 71(2), pages 347-363, June.
    25. Goel, Asvin & Gruhn, Volker, 2008. "A General Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 650-660, December.
    26. Kaoru Fueda & Masaya Iizuka & Yuichi Mori, 2009. "Variable selection in multivariate methods using global score estimation," Computational Statistics, Springer, vol. 24(1), pages 127-144, February.
    27. Henry Kaiser, 1974. "An index of factorial simplicity," Psychometrika, Springer;The Psychometric Society, vol. 39(1), pages 31-36, March.
    28. António Pedro Duarte Silva, 2002. "Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons," Computational Statistics, Springer, vol. 17(2), pages 251-271, July.
    29. Yutaka Kano & Akira Harada, 2000. "Stepwise variable selection in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 7-22, March.
    30. Cadima, Jorge & Cerdeira, J. Orestes & Minhoto, Manuel, 2004. "Computational aspects of algorithms for variable selection in the context of principal components," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 225-236, September.
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    Cited by:

    1. Brusco, Michael J. & Steinley, Douglas, 2011. "Exact and approximate algorithms for variable selection in linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 123-131, January.
    2. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    3. Claudio Morana, 2022. "Euro area inflation and a new measure of core inflation," Working Paper series 22-14, Rimini Centre for Economic Analysis, revised Nov 2023.
    4. Tyler Hunt & Peter Bentler, 2015. "Quantile Lower Bounds to Reliability Based on Locally Optimal Splits," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 182-195, March.
    5. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.
    6. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.

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