Multiple Kernel Learning Improved by MMD

摘要：

When training and testing data are drawn from different distributions, the performance of the classification model will be low. Such a problem usually comes from sample selection bias or transfer learning scenarios. In this paper, we propose a novel multiple kernel learning framework improved by Maximum Mean Discrepancy (MMD) to solve the problem. This new model not only utilizes the capacity of kernel learning to construct a nonlinear hyperplane which maximizes the separation margin, but also reduces the distribution discrepancy between training and testing data simultaneously, which is measured by MMD. This approach is formulated as a bi-objective optimization problem. Then an efficient optimization algorithm based on gradient descent and quadratic programming13is adopted to solve it.Extensive experiments on UCI and text datasets show that the propsed model outperforms traditional multiple kernel learning model in sample selection bias and transrer learning scenarios.

关键词： Kernel Learning Maximum Mean Discrepancy

作者: Jiangtao Ren Zhou Liang Shaofeng Hu

作者单位: School of Software, Sun Yat-sen University, China Department of Computer Science, Sun Yat-sen University, China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 63-74

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）