Journal
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/IJCNN52387.2021.9533502
Keywords
Pinball loss function; Fisher regularization; Support vector machine; Noise insensitivity
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Funding
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [19KJA550002]
- Six Talent Peak Project of Jiangsu Province of China [XYDXX-054]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
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Pin-FisherSVM is a support vector machine method that integrates the pinball loss function and Fisher regularization, exhibiting good performance and statistical separability in noisy environments.
Fisher-regularized support vector machine (FisherSVM) can approximatively fulfill the Fisher criterion and obtain good statistical separability, which is a combined method of the support vector machine and Fisher discriminant analysis. However, the hinge loss function is related to the shortest distance between two-class sets, and FisherSVM may be hence sensitive to noise. To remedy it, the pinball loss function, which is related to the quantile distance, is introduced into FisherSVM and then a Fisher-regularized support vector machine with pinball loss function (Pin-FisherSVM) is proposed, which combines the noise insensitivity of the pinball loss function with the statistical separability of FisherSVM well. Pin-FisherSVM can be cast as a quadratic programming, which results a globally optimal solution. Experimental results on artificial and real-world datasets demonstrate that our proposed method is insensitive to label noise or feature noise. Compared to FisherSVM, the proposed Pin-FisherSVM has the same computational complexity and exhibits superior noise insensitivity.
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