Visualization of Chemical Space using Kernel Based Principal Component Research
B. Firdaus Begam1, J. Rajeswari2
1Dr. B. Firdaus Begam, Assistant Professor, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore (Tamil Nadu), India.
2Dr. J. Rajeswari, Assistant Professor, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore (Tamil Nadu), India.
Manuscript received on 11 September 2019 | Revised Manuscript received on 20 September 2019 | Manuscript Published on 11 October 2019 | PP: 590-593 | Volume-8 Issue-11S September 2019 | Retrieval Number: K109709811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1097.09811S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Principal Component analysis (PCA) is one of the important and popular multivariate statistical methods applied over various data modeling applications. Traditional PCA handles linear variance in molecular descriptors or features. Handling complicated data by standard PCA will not be very helpful. This drawback can be handled by introducing kernel matrix over PCA. Kernel Principal Component Analysis (KPCA) is an extension of conventional PCA which handles non-linear hidden patterns exists in variables. It results in computational efficiency for data analysis and data visualization. In this paper, KPCA has been applied over dug-likeness dataset for visualization of non-linear relations exists in variables.
Keywords: Principal Component Analysis, Kernel Principal Component Analysis, Visualization.
Scope of the Article: Component-Based Software Engineering