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Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System
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DOI：10.1109/TCYB.2016.2590472.png)
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Abstract
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In this paper, a fast kernel ridge regression (KRR) learning algorithm is adopted with O(N) training cost for large-scale active authentication system. A truncated Gaussian radial basis function (TRBF) kernel is also implemented to provide better cost-performance tradeoff. The fast-KRR algorithm along with the TRBF kernel offers computational advantages over the traditional support vector machine (SVM) with Gaussian-RBF kernel while preserving the error rate performance. Experimental results validate the cost-effectiveness of the developed authentication system. In numbers, the fast-KRR learning model achieves an equal error rate (EER) of 1.39% with O(N) training time, while SVM with the RBF kernel shows an EER of 1.41% with O(N-2) training time.
Keywords:
Active authentication
cost-effective
kernel methods
kernel ridge regression (KRR)
keystroke
support vector machine (SVM)
truncated-radial basis function (TRBF)
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