基于共同决策方向矢量的多源迁移及其快速学习方法

多源迁移学习提取了多个相似领域之间有用信息,提高了学习效率,但存在计算核矩阵的空间和时间复杂度较高的问题.提出了一种多源迁移学习方法,该方法基于结构风险最小框架理论,以共同决策方向矢量为基准,将多个相似领域的决策方向矢量嵌入到支持向量机的训练过程中,提高了目标领域分类器的分类性能.并结合核心向量机理论提出了共同决策方向矢量核心向量机,实现对大样本数据集的快速分类学习.模拟和真实数据集实验表明了所提算法的有效性.

Common-Decision-Vector Based Multiple Source Transfer Learning Classification and Its Fast Learning Method

Multiple source transfer learning (MSTL) has been obtaining more and more applications especially from several related source domains to help the learning task on target domain.However,multiple source transfer learning algorithms often deal with the corresponding quadratic programming problems which may suffer a big computational burden caused by the kernel matrix computation.In this paper,a novel common-decision-vector based multiple source transfer classification learning (CDV-MSTL) is proposed which doesn't depend on the intrinsic structure of data.This algorithm is based on the structural risk minimization principle and the SVM like framework,so it has good adaptability and better accuracy.Based on the theory of CVM,CDV-MSTL is extended to its CVM based version which can realize fast training for large scale data.Extensive experiments on synthetic and real-world datasets demonstrate the significant improvement in classification performance obtained by the proposed algorithm over existing MSTL algorithm.