A Rapid Soft Computing Approach to Dimensionality Reduction in Model Construction

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http://hdl.handle.net/10138/310422

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Niskanen , V A 2017 , A Rapid Soft Computing Approach to Dimensionality Reduction in Model Construction . in J Kacprzyk & V Kreinovich (eds) , Uncertainty Modeling : Dedicated to Professor Boris Kovalerchuk on his Anniversary . Studies in Computational Intelligence , vol. 683 , Springer-Verlag , Cham , pp. 169-191 . https://doi.org/10.1007/978-3-319-51052-1_12

Title: A Rapid Soft Computing Approach to Dimensionality Reduction in Model Construction
Author: Niskanen, Vesa A.
Editor: Kacprzyk, J; Kreinovich, V.
Contributor: University of Helsinki, Department of Economics and Management
Publisher: Springer-Verlag
Date: 2017
Language: eng
Number of pages: 22
Belongs to series: Uncertainty Modeling Dedicated to Professor Boris Kovalerchuk on his Anniversary
Belongs to series: Studies in Computational Intelligence
ISBN: 978-3-319-51051-4
978-3-319-51052-1
URI: http://hdl.handle.net/10138/310422
Abstract: A rapid soft computing method for dimensionality reduction of data sets is presented. Traditional approaches usually base on factor or principal component analysis. Our method applies fuzzy cluster analysis and approximate reasoning instead, and thus it is also viable to nonparametric and nonlinear models. Comparisons are drawn between the methods with two empiric data sets.
Subject: 113 Computer and information sciences
112 Statistics and probability
Dimension reduction
Factor analysis
Principal component analysis
Fuzzy cluster anal ysis
Fuzzy reasoning
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