A new principal component analysis by particle swarm optimization with an environmental application for data science
| Autor: | Carlos Martin-Barreiro, Ana B. Nieto-Librero, M. Purificación Galindo-Villardón, Víctor Leiva, John A. Ramirez-Figueroa |
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| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Environmental Engineering 010504 meteorology & atmospheric sciences Computer science 0208 environmental biotechnology Particle swarm optimization Computational intelligence 02 engineering and technology Disjoint sets 01 natural sciences 020801 environmental engineering Reduction (complexity) Local optimum Principal component analysis Environmental Chemistry Safety Risk Reliability and Quality Linear combination 0105 earth and related environmental sciences General Environmental Science Water Science and Technology Curse of dimensionality |
| Zdroj: | Stochastic Environmental Research and Risk Assessment. 35:1969-1984 |
| ISSN: | 1436-3259 1436-3240 |
| DOI: | 10.1007/s00477-020-01961-3 |
| Popis: | In this paper, we propose a new method for disjoint principal component analysis based on an intelligent search. The method consists of a principal component analysis with constraints, allowing us to determine components that are linear combinations of disjoint subsets of the original variables. The effectiveness of the proposed method contributes to solve one of the crucial problems of multivariate analysis, that is, the interpretation of the vectorial subspaces in the reduction of the dimensionality. The method selects the variables that contribute the most to each of the principal components in a clear and direct way. Numerical results are provided to confirm the quality of the solutions attained by the proposed method. This method avoids a local optimum and obtains a high success rate when reaching the best solution, which occurs in all the cases of our simulation study. An illustration with environmental real data shows the good performance of the method and its potential applications. |
| Databáze: | OpenAIRE |
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