NDLI: A new combined KSVM and KFD model for classification and recognition

Content Provider	IEEE Xplore Digital Library
Author	Ksantini, R. Boufama, B. Ahmad, I.S. Khan, N.M.
Copyright Year	2010
Description	Author affiliation: School of Computer Science, University of Windsor, Windsor, ON, Canada N9B 3P4 (Ksantini, R.; Boufama, B.; Ahmad, I.S.; Khan, N.M.)
Abstract	The Kernel Support Vector Machine (KSVM) is a powerful nonlinear classification methodology where, the Support Vectors (SVs) fully describe the decision surface by incorporating local information in the Kernel space. On the other hand, the Kernel Fisher Discriminant(KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. This paper proposes a novel KSVM + KFD model which combines these two methods. This model can be viewed as an extension to the KSVM by incorporating 'global' characteristics of the data to estimate the decision boundary in the Kernel space. On the other hand, this new model could also be considered as an improvement to the KFD by incorporating the Support Vectors (local margin concept) into the KFD formulation. The KSVM + KFD model can be reduced to the classical KSVM model so that existing KSVM software can be used for easy implementation. An extensive comparison of the KSVM + KFD to the KFD, KSVM, Linear Discriminant Analysis (LDA), Linear Support Vector Machine (LSVM) and the combined LSVM and LDA, performed on real data sets, has shown the advantages of our proposed model. In particular, the experiments on face recognition have clearly shown the superiority of the KSVM + KFD over other methods.
Starting Page	188
Ending Page	193
File Size	242868
Page Count	6
File Format	PDF
ISBN	9781424475728
e-ISBN	9781424475735
DOI	10.1109/ICDIM.2010.5664217
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-05
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Accuracy Databases Data models Face Kernel
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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