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IMPROVING THE DIMENSIONALITY REDUCTION OF PCA USING BIVARIATE COPULAS
Keywords:
bivariate copula, Kendall’s tau, dimensionality reduction, correlation, feature extraction, PCADOI:
https://doi.org/10.17654/0972361723015Abstract
Day by day, the size of datasets used in modern solutions is growing up. Thus it is crucial to identify their properties and reduce their dimensions without losing any important information. Many methods have been proposed to deal with this problem, which are divided into two types: feature extraction and feature selection. Among the feature extraction techniques, Principal Component Analysis (PCA) is the most used method. In this study, we develop a new approach, with the intention of improving PCA reduction and information extraction. The proposed method uses bivariate copulas to detect correlation and eliminates it, then it performs PCA on the reduced data. The proposed method is compared against the baseline method PCA and another method that combines multivariate copulas and PCA. The comparison is made using real world data according to the dimensionality reduction, and the classification accuracy of the new reduced data.
Received: November 23, 2022; Accepted: February 7, 2023; Published: February 14, 2023
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