Genetic algorithms and particle swarm optimization for exploratory projection pursuit

Berro, Alain, Larabi, Souâd and Ruiz-Gazen, Anne (2011) Genetic algorithms and particle swarm optimization for exploratory projection pursuit. Annals of Mathematics and Artificial Intelligence, Learning and Intelligent Optimization, vol. 60 (n° 1/2). pp. 153-178.

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Abstract

Exploratory Projection Pursuit (EPP) methods have been developed thirty years ago in the context of exploratory analysis of large data sets. These methods consist in looking for low-dimensional projections that reveal some interesting structure existing in the data set but not visible in high dimension. Each projection is associated with a real valued index which optima correspond to valuable projections. Several EPP indices have been proposed in the statistics literature but the main problem lies in their optimization. In the present paper, we propose to apply Genetic Algorithms (GA) and recent Particle Swarm Optimization (PSO) algorithms to the optimization of several projection pursuit indices. We explain how the EPP methods can be implemented in order to become an ef¿cient and powerful tool for the statistician. We illustrate our proposal on several simulated and real data sets.

Item Type: Article
Language: English
Date: 2011
Refereed: Yes
Uncontrolled Keywords: Clustering, exploratory projection pursuit, genetic algorithm, particle swarm optimization
Subjects: H- INFORMATIQUE
Divisions: Institut de Recherche en Informatique de Toulouse
Site: UT1
Date Deposited: 05 Dec 2018 15:17
Last Modified: 20 Feb 2019 15:24
OAI ID: BibTeX_BeLaRu2011.1
URI: http://publications.ut-capitole.fr/id/eprint/26559

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