دورية أكاديمية
Kernel partial diagnostic robust potential to handle high-dimensional and irregular data space on near infrared spectral data
| العنوان: | Kernel partial diagnostic robust potential to handle high-dimensional and irregular data space on near infrared spectral data |
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| المؤلفون: | Divo Dharma Silalahi, Habshah Midi, Jayanthi Arasan, Mohd Shafie Mustafa, Jean-Pierre Caliman |
| المصدر: | Heliyon, Vol 6, Iss 1, Pp e03176- (2020) |
| بيانات النشر: | Elsevier, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Science (General) LCC:Social sciences (General) |
| مصطلحات موضوعية: | Analytical chemistry, Near infrared, Spectral data, Partial least squares, Diagnostic robust generalized potential, Nonlinear, Science (General), Q1-390, Social sciences (General), H1-99 |
| الوصف: | In practice, the collected spectra are very often composes of complex overtone and many overlapping peaks which may lead to misinterpretation because of its significant nonlinear characteristics. Using linear solution might not be appropriate. In addition, with a high-dimension of dataset due to large number of observations and data points the classical multiple regressions will neglect to fit. These complexities commonly will impact to multicollinearity problem, furthermore the risk of contamination of multiple outliers and high leverage points also increases. To address these problems, a new method called Kernel Partial Diagnostic Robust Potential (KPDRGP) is introduced. The method allows the nonlinear solution which maps nonlinearly the original input X matrix into higher dimensional feature mapping with corresponds to the Reproducing Kernel Hilbert Spaces (RKHS). In dimensional reduction, the method replaces the dot products calculation of elements in the mapped data to a nonlinear function in the original input space. To prevent the contamination of the multiple outlier and high leverage points the robust procedure using Diagnostic Robust Generalized Potentials (DRGP) algorithm was used. The results verified that using the simulation and real data, the proposed KPDRGP method was superior to the methods in the class of non-kernel and some other robust methods with kernel solution. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2405-8440 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S2405844020300219Test; https://doaj.org/toc/2405-8440Test |
| DOI: | 10.1016/j.heliyon.2020.e03176 |
| الوصول الحر: | https://doaj.org/article/9fd48ad127224cacb7c9e439e1dd4384Test |
| رقم الانضمام: | edsdoj.9fd48ad127224cacb7c9e439e1dd4384 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24058440 |
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| DOI: | 10.1016/j.heliyon.2020.e03176 |