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Dimension reduction by identifying and removing redundant variables using copula function | ||
| Journal of Mathematical Modeling | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 01 تیر 1404 اصل مقاله (241.88 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2025.28169.2484 | ||
| نویسندگان | ||
| Kianoush Fathi Vajargah* 1؛ Hamid Mottaghi Golshan2؛ Fazel Badakhshan1 | ||
| 1Department of Statistics, Islamic Azad University, North Tehran Branch, Tehran, Iran | ||
| 2Department of Mathematics, Islamic Azad University, Shahriar Branch, Shahriar, Iran | ||
| چکیده | ||
| In today's world, rapid developments in science and engineering are increasingly adding up to larger amounts of data; as a result, numerous problems have emerged in the analysis of big data. Hence, data dimensionality reduction can accelerate data analysis and even yield better results without losing any useful data. A copula represents an appropriate model of dependence to compare multivariate distributions and better detect the relationships of data. Therefore, a copula is employed in this study to identify and delete noisy data from the original data. Then, it is compared to the principal component analysis to show its superiority. | ||
| کلیدواژه‌ها | ||
| Gaussian copula function (normal)؛ Classification؛ Principal component analysis method (PCA)؛ Data Analysis؛ Parkinson’s Disease | ||
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آمار تعداد مشاهده مقاله: 4 تعداد دریافت فایل اصل مقاله: 14 |
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