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Dimension Reduction Big Data Using Recognition of Data Features Based on Copula Function and Principal Component Analysis

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  • Fazel Badakhshan Farahabadi
  • Kianoush Fathi Vajargah
  • Rahman Farnoosh

Abstract

Nowadays, data are generated in the world with high speed; therefore, recognizing features and dimensions reduction of data without losing useful information is of high importance. There are many ways to dimension reduction, including principal component analysis (PCA) method, which is by identifying effective dimensions in an acceptable level, reducing dimension of data. In the usual method of principal component analysis, data are usually normal, or we normalize data; then, the principal component analysis method is used. Many studies have been done on the principal component analysis method as a step of data preparation. In this paper, we propose a method that improves the principal component analysis method and makes data analysis easier and more efficient. Also, we first identify the relationships between the data by fitting the multivariate copula function to data and simulate new data using the estimated parameters; then, we reduce the dimensions of new data by principal component analysis method; the aim is to improve the performance of the principal component analysis method to find effective dimensions.

Suggested Citation

  • Fazel Badakhshan Farahabadi & Kianoush Fathi Vajargah & Rahman Farnoosh, 2021. "Dimension Reduction Big Data Using Recognition of Data Features Based on Copula Function and Principal Component Analysis," Advances in Mathematical Physics, John Wiley & Sons, vol. 2021(1).
    • Handle: RePEc:wly:jnlamp:v:2021:y:2021:i:1:n:9967368
    DOI: 10.1155/2021/9967368
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    References listed on IDEAS

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    1. Johnstone, Iain M. & Lu, Arthur Yu, 2009. "On Consistency and Sparsity for Principal Components Analysis in High Dimensions," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 682-693.
    2. Johannes Forkman & Julie Josse & Hans-Peter Piepho, 2019. "Hypothesis Tests for Principal Component Analysis When Variables are Standardized," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 289-308, June.
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