核分布一致局部领域适应学习

针对领域适应学习(Domain adaptation learning, DAL)问题,提出一种核分布一致局部领域适应学习机(Kernel distribution consistency based local domain adaptation classifier, KDC-LDAC),在某个通用再生核Hilbert空间(Universally reproduced kernel Hilbert space, URKHS),基于结构风险最小化模型, KDC-LDAC首先学习一个核分布一致正则化支持向量机(Support vector machine, SVM),对目标数据进行初始划分; 然后,基于核局部学习思想,对目标数据类别信息进行局部回归重构; 最后,利用学习获得的类别信息,在目标领域训练学习一个适于目标判别的分类器.人 造和实际数据集实验结果显示,所提方法具有优化或可比较的领域适应学习性能.

Kernel Distribution Consistency Based Local Domain Adaptation Learning

In allusion to domain adaptation learning (DAL) problems, this paper proposes a novel so-called kernel distribution consistency based local domain adaptation classifier (KDC-LDAC). Firstly, in some universally reproduced kernel Hilbert space (URKHS), the KDC-LDAC trains a kernel distribution consistency regularized domain adaptation support vector machine (SVM) based on the structure risk minimization model, which extends the formulation of classical SVMs to the domain adaptation learning schema. And secondly, according to the idea of local learning, the proposed method predicts the label of each data point in target domain based on its neighbors and their labels in the URKHS. The last but not least, the KDC-LDACs learning a discriminant function to classify the unseen data in target domain with training data well predicted in the local learning procedure. Experimental results on artificial and real world problems show the advantages or comparable effectiveness of the proposed approach compared to related approaches.