A new principal component analysis by particle swarm optimization with an environmental application for data science

John A. Ramirez-Figueroa, Carlos Martin-Barreiro, Ana B. Nieto-Librero, VICTOR ELISEO LEIVA SANCHEZ, M. Purificación Galindo-Villardón

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
JournalStochastic Environmental Research and Risk Assessment
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • Constrained binary particle swarm optimization
  • Data mining
  • Disjoint principal components
  • Evolutionary computation
  • R software
  • Singular value decomposition

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