核典型关联性分析相关特征提取与核逻辑斯蒂回归域自适应学习

Domain Adaptation Learning with Kernel Logistic Regression and Kernel Canonical Correlation Analysis

本文提出了一种利用核典型关联性分析提取源域目标域最大相关特征,使用核逻辑斯蒂回归模型进行域自适应学习的算法,该算法称为KCCA-DAML(Kernel Canonical Correlation Analysis for Domain Adaptation Learning).该算法基于特征集关联性分析,有效的减小源域和目标域的概率分布差异性,利用提取的最大相关特征通过核逻辑斯蒂回归模型实现源域到目标域的跨域学习.实验比较源域数据上核逻辑斯蒂学习模型、目标域上核逻辑斯蒂学习模型、源域和目标域上核逻辑斯蒂学习模型和KCCA-DAML模型,结果显示KCCA-DAML在真实数据集上成功的实现了跨域学习.

The domain adaptive learning algorithm using kernel logistic regression model is proposed. The proposed ap- proach use kernel canonical correlation analysis to extract the maximum relevant features of the source and target domain. We dub it as KCCA-DAML（Kernel Canonical Correlation Analysis for Domain Adaptation Learning, KCCA-DAML）. Our algorithm is based on canonical correlation analysis, which simultaneously minimizes the incompatibility among source features ,target features and instance labels,extract maximum relevant features from source features ,target features and instance labels, and use kernel lo- gistic regression domain adaptation learning. In experimental comparison of the kernel logistic model and KCCA-DAML model on source domain data,the target domain data,source and the target domain data,we demonstrate the power of our techniques with the following real-world data sets：Reuters 20 Newsgroups ,MNIST handwritten-digits and UCI Dermatology.