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Optimal column subset selection for image classification by genetic algorithms

Author

Listed:
  • Pavel Krömer

    (VŠB-Technical University of Ostrava)

  • Jan Platoš

    (VŠB-Technical University of Ostrava)

  • Jana Nowaková

    (VŠB-Technical University of Ostrava)

  • Václav Snášel

    (VŠB-Technical University of Ostrava)

Abstract

Many problems in operations research can be solved by combinatorial optimization. Fixed-length subset selection is a family of combinatorial optimization problems that involve selection of a set of unique objects from a larger superset. Feature selection, p-median problem, and column subset selection problem are three examples of hard problems that involve search for fixed-length subsets. Due to their high complexity, exact algorithms are often infeasible to solve real-world instances of these problems and approximate methods based on various heuristic and metaheuristic (e.g. nature-inspired) approaches are often employed. Selecting column subsets from massive data matrices is an important technique useful for construction of compressed representations and low rank approximations of high-dimensional data. Search for an optimal subset of exactly k columns of a matrix, $$A^{m\times n}$$ A m × n , $$k

Suggested Citation

  • Pavel Krömer & Jan Platoš & Jana Nowaková & Václav Snášel, 2018. "Optimal column subset selection for image classification by genetic algorithms," Annals of Operations Research, Springer, vol. 265(2), pages 205-222, June.
    • Handle: RePEc:spr:annopr:v:265:y:2018:i:2:d:10.1007_s10479-016-2331-0
    DOI: 10.1007/s10479-016-2331-0
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    References listed on IDEAS

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    1. Mladenovic, Nenad & Brimberg, Jack & Hansen, Pierre & Moreno-Perez, Jose A., 2007. "The p-median problem: A survey of metaheuristic approaches," European Journal of Operational Research, Elsevier, vol. 179(3), pages 927-939, June.
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    Cited by:

    1. Jin Zhang & Cong Wang & Guoqing Chen, 2021. "A Review Selection Method for Finding an Informative Subset from Online Reviews," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 280-299, January.

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