多核局部领域适应学习

领域适应(或跨领域)学习旨在利用源领域(或辅助领域)中带标签样本来学习一种鲁棒的目标分类器,其关键问题在于如何最大化地减小领域间的分布差异.为了有效解决领域间特征分布的变化问题,提出一种三段式多核局部领域适应学习(multiple kernel local leaning-based domain adaptation,简称MKLDA)方法:1)基于最大均值差(maximum mean discrepancy,简称MMD)度量准则和结构风险最小化模型,同时,学习一个再生多核Hilbert空间和一个初始的支持向量机(support vector machine,简称SVM),对目标领域数据进行初始划分；2)在习得的多核Hilbert空间,对目标领域数据的类别信息进行局部重构学习；3)最后,利用学习获得的类别信息,在目标领域训练学习一个鲁棒的目标分类器.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.

Multiple Kernel Local Leaning-Based Domain Adaptation

Domain adaptation(or cross domain) learning(DAL) aims to learn a robust target classifier for the target domain,which has none or a few labeled samples,by leveraging labeled samples from the source domain(or auxiliary domain).The key challenge in DAL is how to minimize the maximum distribution distance among different domains.To address the considerable change between feature distributions of different domains,this paper proposes a three-stage multiple kernel local learning-based domain adaptation(MKLDA) scheme:1) MKLDA simultaneously learns a reproduced multiple kernel Hilbert space and a initial support vector machine(SVM) by minimizing both the structure risk functional and the maximum mean discrepancy(MMD) between different domains,thus implementing the initial separation of patterns from target domain;2) By employing the idea of local learning-based method,MKLDA predicts the label of each data point in target domain based on its neighbors and their labels in the kernel Hilbert space learned in 1);And 3) MKLDA learns a robust kernel classifier to classify the unseen data in target domain with training data well predicted in 2).Experimental results on real world problems show the outperformed or comparable effectiveness of the proposed approach compared to related approaches.