HYBRID DIMENSIONALITY REDUCTION METHOD USING KAISER COMPONENT ANALYSIS AND INDEPENDENT COMPONENT ANALYSIS
K. Arunasakthi, KLN College of Engineering; J.Sulthan Alikhan ,
Dimensionality reduction, Kaiser Component Analysis, Independent Component Analysis, Stand alone and Hybrid methods, SVM classification.
Dimensionality Reduction is the process of extracting the more relevant information. Conventional dimensionality reduction is categorized into two methods like Stand alone and Hybrid method. Standalone dimensionality reduction reduces the dimensions based on a single criterion whereas Hybrid method combines two or more criterion. In this paper, we proposed new hybrid method for dimensionality reduction Using Kaiser Component Analysis (KCA) and Independent Component Analysis (ICA). Kaiser component Analysis extracts the uncorrelated information and Independent Component Analysis maximizing the Independency among the data. The hybrid method using these Kaiser Component Analysis and Independent Component Analysis achieves both correlations and Independency among the Information and it is applied on SVM classification. The result improves the accuracy of the classification.
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Paper ID: GRDCF002055
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 415 - 420
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 415 - 420