论文标题
多任务非平行支持向量机进行分类
Multi-task nonparallel support vector machine for classification
论文作者
论文摘要
直接多任务双支持矢量机（DMTSVM）探讨了多个相关任务之间的共享信息，然后产生更好的概括性能。但是，在解决双重问题时，它包含矩阵反转操作，因此它花费了很大的运行时间。此外，在非线性情况下不能直接使用内核技巧。为了有效避免上述问题，本文提出了一种新型的多任务非平行支持向量机（MTNPSVM），包括线性和非线性案例。通过引入对Epsilon的不敏感损失，而不是DMTSVM中的正方形损失，MTNPSVM有效地避免了矩阵反转操作，并充分利用了内核技巧。进一步讨论了模型的理论意义。为了进一步提高计算效率，在解决双重问题时采用了乘数的交替方向方法（ADMM）。提供了算法的计算复杂性和收敛性。另外，进一步探讨了模型中参数的属性和灵敏度。与最先进的算法相比，15个基准数据集和十二个图像数据集的实验结果证明了MTNPSVM的有效性。最后，它应用于真正的中国葡萄酒数据集，并验证其有效性。
Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the dual problems, so it costs much running time. Moreover, kernel trick cannot be directly utilized in the nonlinear case. To effectively avoid above problems, a novel multi-task nonparallel support vector machine (MTNPSVM) including linear and nonlinear cases is proposed in this paper. By introducing epsilon-insensitive loss instead of square loss in DMTSVM, MTNPSVM effectively avoids matrix inversion operation and takes full advantage of the kernel trick. Theoretical implication of the model is further discussed. To further improve the computational efficiency, the alternating direction method of multipliers (ADMM) is employed when solving the dual problem. The computational complexity and convergence of the algorithm are provided. In addition, the property and sensitivity of the parameter in model are further explored. The experimental results on fifteen benchmark datasets and twelve image datasets demonstrate the validity of MTNPSVM in comparison with the state-of-the-art algorithms. Finally, it is applied to real Chinese Wine dataset, and also verifies its effectiveness.
