Enhanced algorithm for high-dimensional data classification |
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| Affiliation: | 1. School of Computer and Software Engineering, Xihua University, Chengdu 610039, China;2. School of Digital Media, Jiangnan University, Wuxi 214122, China;1. Department of Mathematics, University of Delhi, Delhi, India;2. Department of Mathematics, Deen Dayal Upadhyaya College, University of Delhi, Delhi, India;1. Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands;3. Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy;4. INRA UMR 782 GMPA, 1 Avenue Lucien Brétignières, 78850 Thiverval-Grignon, France;1. School of Software Engineering, Chongqing University, Chongqing 400044, PR China;2. School of Computing, National University of Singapore, Singapore 117417, Singapore;3. School of Information Science and Engineering, Lanzhou University, Gansu 730000, PR China;4. Faculty of Computer and Information Science, Southwest University, Chongqing 400715, PR China;5. Faculty of Engineering, The University of Sydney, Sydney 2006, Australia;1. Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;2. Department of IT, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;3. Department of CSE, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;4. Department of CSE, Ramco Institute of Technology, Rajapalayam, India;1. Electric and Electronics Engineering Department, Bilecik ?eyh Edebali University, Turkey;2. Computer Engineering Department, Dumlup?nar University, Turkey |
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| Abstract: | Minimum class variance support vector machine (MCVSVM) and large margin linear projection (LMLP) classifier, in contrast with traditional support vector machine (SVM), take the distribution information of the data into consideration and can obtain better performance. However, in the case of the singularity of the within-class scatter matrix, both MCVSVM and LMLP only exploit the discriminant information in a single subspace of the within-class scatter matrix and discard the discriminant information in the other subspace. In this paper, a so-called twin-space support vector machine (TSSVM) algorithm is proposed to deal with the high-dimensional data classification task where the within-class scatter matrix is singular. TSSVM is rooted in both the non-null space and the null space of the within-class scatter matrix, takes full advantage of the discriminant information in the two subspaces, and so can achieve better classification accuracy. In the paper, we first discuss the linear case of TSSVM, and then develop the nonlinear TSSVM. Experimental results on real datasets validate the effectiveness of TSSVM and indicate its superior performance over MCVSVM and LMLP. |
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| Keywords: | Machine learning Supervised learning Kernel methods Support vector machine |
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