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Multiple Kernel-Based Discriminant Analysis via Support Vectors for Dimension Reduction | IEEE Journals & Magazine | IEEE Xplore

Multiple Kernel-Based Discriminant Analysis via Support Vectors for Dimension Reduction


SVM direction, LDA direction and our method direction in the projected Hilbert space.

Abstract:

Kernel-based discriminant analysis is an effective nonlinear mechanism for pattern analysis. Conventional kernel-based discriminant analysis mainly based on a single kern...Show More

Abstract:

Kernel-based discriminant analysis is an effective nonlinear mechanism for pattern analysis. Conventional kernel-based discriminant analysis mainly based on a single kernel function may be insufficient when dealing with datasets with complicated geometric structures. A combination of multiple kernels is able to represent the complementary information of the original data from multiple views and thereby improves recognition performance. However, the discriminant analysis methods based on the combination of multiple kernels face the challenges of optimizing the weights of the “base kernels” and the heavy computational burden. To address these challenges, this paper proposes a novel multi-kernel discriminant analysis method based on support vectors (MKDASV) to represent the data structure more effectively by incorporating the between-class and within-class information. First, the multi-kernel SVM algorithm is utilized to obtain the weight of each “base kernel” and the support vectors; and then the criteria of discriminant analysis method are constructed by taking into account both the margin maximizing classification theory of SVM and the expression of the within-class scatter in LDA algorithm; and finally, to effectively reduce the amount of computation, only the support vectors are used as the training samples to participate in the dimensionality reduction operation. The experimental results on six standard databases validated that our proposed method outperformed the other five methods in terms of classification accuracy and the computational efficiency as well.
SVM direction, LDA direction and our method direction in the projected Hilbert space.
Published in: IEEE Access ( Volume: 7)
Page(s): 35418 - 35430
Date of Publication: 10 March 2019
Electronic ISSN: 2169-3536

Funding Agency:

Author image of Shan Zeng
College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
Shan Zeng received the B.E. degree in mechanical engineering and the M.E. degree in mechatronics engineering from Wuhan Polytechnic University, in 2003 and 2009, respectively, and the Ph.D. degree in pattern recognition and intelligent systems from the Huazhong University of Science and Technology, in 2012. In 2003, he joined the College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China, wher...Show More
Shan Zeng received the B.E. degree in mechanical engineering and the M.E. degree in mechatronics engineering from Wuhan Polytechnic University, in 2003 and 2009, respectively, and the Ph.D. degree in pattern recognition and intelligent systems from the Huazhong University of Science and Technology, in 2012. In 2003, he joined the College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China, wher...View more
Author image of Chongjun Gao
School of Automation, Huazhong University of Science and Technology, Wuhan, China
Chongjun Gao received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017. He is currently pursuing the M.S. degree in control engineering with the Huazhong University of Science and Technology, Wuhan. His research interests include image processing, hyperspectral image analysis, computer vision, and machine learning.
Chongjun Gao received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017. He is currently pursuing the M.S. degree in control engineering with the Huazhong University of Science and Technology, Wuhan. His research interests include image processing, hyperspectral image analysis, computer vision, and machine learning.View more
Author image of Xiuying Wang
School of Computer Science, The University of Sydney, Sydney, NSW, Australia
Xiuying Wang (M’16) received the Ph.D. degree in computer science from the Multimedia Laboratory, School of Computer Science, The University of Sydney, Australia, where she is currently an Associate Professor and also the Associate Director. Her research interests include biomedical data computing and visual analytics, biomedical image registration, identification, clustering, and segmentation.
Xiuying Wang (M’16) received the Ph.D. degree in computer science from the Multimedia Laboratory, School of Computer Science, The University of Sydney, Australia, where she is currently an Associate Professor and also the Associate Director. Her research interests include biomedical data computing and visual analytics, biomedical image registration, identification, clustering, and segmentation.View more
Author image of Liang Jiang
College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
Liang Jiang received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017, where he is currently pursuing the M.S. degree in mathematics and computer science. His research interests include image processing, biometric identification, computer vision, and deep learning.
Liang Jiang received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017, where he is currently pursuing the M.S. degree in mathematics and computer science. His research interests include image processing, biometric identification, computer vision, and deep learning.View more
Author image of Dagan Feng
School of Computer Science, The University of Sydney, Sydney, NSW, Australia
(David) Dagan Feng (F’03) received the Ph.D. degree in computer science from the University of California, Los Angeles, CA, USA, in 1988. He has been the Associate Dean of the Faculty of Science, The University of Sydney. He has been the Chair Professor, Advisory Professor, Guest Professor, and Adjunct Professor or Chief Scientist in different world-known universities and institutes. He is the Founder and the Director of ...Show More
(David) Dagan Feng (F’03) received the Ph.D. degree in computer science from the University of California, Los Angeles, CA, USA, in 1988. He has been the Associate Dean of the Faculty of Science, The University of Sydney. He has been the Chair Professor, Advisory Professor, Guest Professor, and Adjunct Professor or Chief Scientist in different world-known universities and institutes. He is the Founder and the Director of ...View more

Author image of Shan Zeng
College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
Shan Zeng received the B.E. degree in mechanical engineering and the M.E. degree in mechatronics engineering from Wuhan Polytechnic University, in 2003 and 2009, respectively, and the Ph.D. degree in pattern recognition and intelligent systems from the Huazhong University of Science and Technology, in 2012. In 2003, he joined the College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China, where he is currently an Associate Professor. From 2015 to 2016, he was a Visiting Scholar with Macau University and The University of Sydney. His research interests include pattern recognition, machine learning, image processing, and hyperspectral imaging, with their applications in nondestructive testing of food quality.
Shan Zeng received the B.E. degree in mechanical engineering and the M.E. degree in mechatronics engineering from Wuhan Polytechnic University, in 2003 and 2009, respectively, and the Ph.D. degree in pattern recognition and intelligent systems from the Huazhong University of Science and Technology, in 2012. In 2003, he joined the College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China, where he is currently an Associate Professor. From 2015 to 2016, he was a Visiting Scholar with Macau University and The University of Sydney. His research interests include pattern recognition, machine learning, image processing, and hyperspectral imaging, with their applications in nondestructive testing of food quality.View more
Author image of Chongjun Gao
School of Automation, Huazhong University of Science and Technology, Wuhan, China
Chongjun Gao received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017. He is currently pursuing the M.S. degree in control engineering with the Huazhong University of Science and Technology, Wuhan. His research interests include image processing, hyperspectral image analysis, computer vision, and machine learning.
Chongjun Gao received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017. He is currently pursuing the M.S. degree in control engineering with the Huazhong University of Science and Technology, Wuhan. His research interests include image processing, hyperspectral image analysis, computer vision, and machine learning.View more
Author image of Xiuying Wang
School of Computer Science, The University of Sydney, Sydney, NSW, Australia
Xiuying Wang (M’16) received the Ph.D. degree in computer science from the Multimedia Laboratory, School of Computer Science, The University of Sydney, Australia, where she is currently an Associate Professor and also the Associate Director. Her research interests include biomedical data computing and visual analytics, biomedical image registration, identification, clustering, and segmentation.
Xiuying Wang (M’16) received the Ph.D. degree in computer science from the Multimedia Laboratory, School of Computer Science, The University of Sydney, Australia, where she is currently an Associate Professor and also the Associate Director. Her research interests include biomedical data computing and visual analytics, biomedical image registration, identification, clustering, and segmentation.View more
Author image of Liang Jiang
College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
Liang Jiang received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017, where he is currently pursuing the M.S. degree in mathematics and computer science. His research interests include image processing, biometric identification, computer vision, and deep learning.
Liang Jiang received the B.S. degree in information and computing science from Wuhan Polytechnic University, Wuhan, China, in 2017, where he is currently pursuing the M.S. degree in mathematics and computer science. His research interests include image processing, biometric identification, computer vision, and deep learning.View more
Author image of Dagan Feng
School of Computer Science, The University of Sydney, Sydney, NSW, Australia
(David) Dagan Feng (F’03) received the Ph.D. degree in computer science from the University of California, Los Angeles, CA, USA, in 1988. He has been the Associate Dean of the Faculty of Science, The University of Sydney. He has been the Chair Professor, Advisory Professor, Guest Professor, and Adjunct Professor or Chief Scientist in different world-known universities and institutes. He is the Founder and the Director of the Biomedical and Multimedia Information Technology Research Group, The University of Sydney. He is currently the Director (Research) of the Institute of Biomedical Engineering and Technology, and the Academic Director of the USYD-SJTU Joint Research Alliance. He has been elected as a Fellow of the ACS (Australia), the HKIE (Hong Kong), the IET (UK), the IEEE (USA), and the Australian Academy of Technological Sciences and Engineering. He has served as the Chair or Editor of different committees and key journals in the area.
(David) Dagan Feng (F’03) received the Ph.D. degree in computer science from the University of California, Los Angeles, CA, USA, in 1988. He has been the Associate Dean of the Faculty of Science, The University of Sydney. He has been the Chair Professor, Advisory Professor, Guest Professor, and Adjunct Professor or Chief Scientist in different world-known universities and institutes. He is the Founder and the Director of the Biomedical and Multimedia Information Technology Research Group, The University of Sydney. He is currently the Director (Research) of the Institute of Biomedical Engineering and Technology, and the Academic Director of the USYD-SJTU Joint Research Alliance. He has been elected as a Fellow of the ACS (Australia), the HKIE (Hong Kong), the IET (UK), the IEEE (USA), and the Australian Academy of Technological Sciences and Engineering. He has served as the Chair or Editor of different committees and key journals in the area.View more

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