A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis
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Publication:1623574
DOI10.1016/j.csda.2014.03.001zbMath1506.62029OpenAlexW2071716418MaRDI QIDQ1623574
Publication date: 23 November 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2014.03.001
Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Approximation methods and heuristics in mathematical programming (90C59) Applications of statistics to psychology (62P15)
Related Items (6)
A cooperative particle swarm optimization with constriction factor based on simulated annealing ⋮ Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm ⋮ Integrating linear discriminant analysis, polynomial basis expansion, and genetic search for two-group classification ⋮ Stochastic correlation coefficient ensembles for variable selection ⋮ Bayesian variable selection for globally sparse probabilistic PCA ⋮ Maximum relevance minimum common redundancy feature selection for nonlinear data
Uses Software
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