The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A kernel path algorithm for support vector machines
Proceedings of the 24th international conference on Machine learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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The One-Class Support Vector Machine (OC-SVM) is an unsupervised learning algorithm, identifying unusual or outlying points (outliers) from a given dataset. In OC-SVM, it is required to set the regularization hyperparameter and kernel hyperparameter in order to obtain a good estimate. Generally, cross-validation is often used which requires multiple runs with different hyperparameters, making it very slow. Recently, the solution path algorithm becomes popular. It can obtain every solution for all hyperparameters in a single run rather than re-solve the optimization problem multiple times. Generalizing from previous algorithms for solution path in SVMs, this paper proposes a complete set of solution path algorithms for OC-SVM, including a 茂戮驴-path algorithm and a kernel-path algorithm. In the kernel-path algorithm, a new method is proposed to avoid the failure of algorithm due to indefinite matrix . Using those algorithms, we can obtain the optimum hyperparameters by computing an entire path solution with the computational cost O(n2+ cnm3) on 茂戮驴-path algorithm or O(cn3+ cnm3) on kernel-path algorithm (c: constant, n: the number of sample, m: the number of sample which on the margin).