基于多核学习算法的潜在域无监督域自适应  

POTENTIAL DOMAIN UNSUPERVISED DOMAIN ADAPTIVE BASED ON MULTIPLE KERNEL LEARNING ALGORITHM

作　　者：姜磊 章小卫 Jiang Lei;Zhang Xiaowei(Jiangsu College of Tourism,Yangzhou 225131,Jiangsu,China;Yangzhou University,Yangzhou 225009,Jiangsu,China)

机构地区：[1]江苏旅游职业学院,江苏扬州225131 [2]扬州大学,江苏扬州225009

出　　处：《计算机应用与软件》2025年第2期314-322,共9页Computer Applications and Software

基　　金：国家自然科学基金面上项目(61872313,61872312)。

摘　　要：为了提升无监督域自适应性能,提出一种基于多核学习算法的潜在域无监督域自适应方法。提出三个潜在域发现准则:单个潜在目标域中数据紧致性和显著性的最大化,以及潜在目标域到源域的总散度最小化。将学习到的潜在特征空间上的投影源域数据视为源域的不同视图,缩小源域和特定潜在目标域之间的差异。在不同的视觉识别任务上的实验结果表明,该算法具有更好的分类精度与鲁棒性。In order to improve the adaptive performance of unsupervised domain,a potential domain unsupervised domain adaptive method based on multiple kernel learning algorithm is proposed.Three criteria for potential domain discovery were proposed:the maximization of data compactness and significance in a single potential target domain,and the minimization of the total divergence from potential target domain to source domain.The projected source data in the potential feature space was regarded as different views of the source domain,which reduced the difference between the source domain and the specific potential target domain.The experimental results on different tasks show that the proposed algorithm has better classification accuracy and robustness.

关 键 词：无监督 域自适应 多核学习 潜在域 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]