MSI '00 Proceedings of the conference, volume III (in honor of Professor Minoru Siotani on his 70th birthday on Multivariate statistical analysis
Wrappers for Performance Enhancements and Oblivious Decision Graphs.
Wrappers for Performance Enhancements and Oblivious Decision Graphs.
Analysis of new variable selection methods for discriminant analysis
Computational Statistics & Data Analysis
A graph approach to generate all possible regression submodels
Computational Statistics & Data Analysis
A simple method for screening variables before clustering microarray data
Computational Statistics & Data Analysis
Exact and approximate algorithms for variable selection in linear discriminant analysis
Computational Statistics & Data Analysis
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A variable selection problem is analysed for use in Principal Component Analysis (PCA). In this case, the set of original variables is divided into disjoint groups. The problem resides in the selection of variables, but with the restriction that the set of variables that is selected should contain at least one variable from each group. The objective function under consideration is the sum of the first eigenvalues of the correlation matrix of the subset of selected variables. This problem, with no known prior references, has two further difficulties, in addition to that of the variable selection problem: the evaluation of the objective function and the restriction that the subset of selected variables should also contain elements from all of the groups. Two Branch & Bound methods are proposed to obtain exact solutions that incorporate two strategies: the first one is the use of ''fast'' guide functions as alternatives to the objective function; the second one is the preselection of variables that help to comply with the latter restriction. From the computational tests, it is seen that both strategies are very efficient and achieve significant reductions in calculation times.