Nonparallel plane proximal classifier

We observed that the two costly optimization problems of twin support vector machine (TWSVM) classifier can be avoided by introducing a technique as used in proximal support vector machine (PSVM) classifier. With this modus operandi we formulate a much simpler nonparallel plane proximal classifier (...

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Bibliographic Details
Published in:Signal processing 2009-04, Vol.89 (4), p.510-522
Main Authors: Ghorai, Santanu, Mukherjee, Anirban, Dutta, Pranab K.
Format: Article
Language:English
Subjects:
Applied sciences
Classifiers
Computation
Computer programs
Exact sciences and technology
Formulations
Information, signal and communications theory
Matlab
Nonparallel plane
Optimization
Pattern classification
Pattern recognition
Planes
Proximal classifier
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Support vector machines
Telecommunications and information theory
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Summary:We observed that the two costly optimization problems of twin support vector machine (TWSVM) classifier can be avoided by introducing a technique as used in proximal support vector machine (PSVM) classifier. With this modus operandi we formulate a much simpler nonparallel plane proximal classifier (NPPC) for speeding up the training of it by reducing significant computational burden over TWSVM. The formulation of NPPC for binary data classification is based on two identical mean square error (MSE) optimization problems which lead to solving two small systems of linear equations in input space. Thus it eliminates the need of any specialized software for solving the quadratic programming problems (QPPs). The formulation is also extended for nonlinear kernel classifier. Our computations show that a MATLAB implementation of NPPC can be trained with a data set of 3 million points with 10 attributes in less than 3 s. Computational results on synthetic as well as on several bench mark data sets indicate the advantages of the proposed classifier in both computational time and test accuracy. The experimental results also indicate that performances of classifiers obtained by MSE approach are sufficient in many cases than the classifiers obtained by standard SVM approach.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2008.10.002