计算机科学与探索2024，Vol.18Issue(3)：674-692,19.DOI:10.3778/j.issn.1673-9418.2211120
可能性分布距离度量:一种鲁棒的域适应学习方法
Possibilistic Distribution Distance Measure:Robust Domain Adaptation Learning Method
摘要
Abstract
Domain adaptation(DA)aims to solve the problem of inconsistent distribution between training dataset and test dataset,which has attracted extensive attention.Most of the existing DA methods solve this problem by the maximum mean discrepancy(MMD)criterion or its variants.However,the noise data may lead to a significant drift of domain mean,which will reduce the performance of MMD and its variants to some extent.To this end,this paper proposes a robust domain adaptation method with possibilistic distribution distance measure.Firstly,the traditional MMD criterion is transformed into a new possibilistic clustering model,which aims to reduce the impact from noise data.This paper constructs a robust possibilistic distribution distance measure(P-DDM)criterion.It further improves the robust effectiveness of domain distribution alignment by adding the fuzzy entropy regularization term.Secondly,a domain adaptation visual classifier based on P-DDM(C-PDDM)is proposed.It adopts a graphical Laplacian matrix for preserving the geometric consistency of data in source domain and target domain.It can improve the label propagation performance.In order to improve generalization,it maximizes the use of source domain discrimination information to minimize the domain discrimination error.Theoretical analysis confirms that the proposed P-DDM is an upper bound of the traditional distribution distance measurement method MMD criterion under certain conditions.Therefore,minimizing the P-DDM can effectively optimize the MMD objective.Finally,it is compared with several represen-tative domain adaptation methods,and the experimental results on 6 visual benchmark datasets(Office31,Office-Caltech,Office-Home,PIE,MNIST-UPS,and COIL20)show that the proposed method achieves an average improve-ment of about 5%on generalization performance and an average improvement of about 10%on robustness performance.关键词
领域适应(DA)/可能性聚类/最大均值差(MMD)/模糊熵Key words
domain adaptation(DA)/probabilistic clustering/maximum mean discrepancy(MMD)/fuzzy entropy分类
信息技术与安全科学引用本文复制引用
但雨芳,陶剑文..可能性分布距离度量:一种鲁棒的域适应学习方法[J].计算机科学与探索,2024,18(3):674-692,19.基金项目
浙江省教育委员会项目(Y202250345) （Y202250345）
宁波市自然科学基金(2022J180).This work was supported by the Project of Educational Committee of Zhejiang Province(Y202250345),and the Natural Science Foun-dation of Ningbo(2022J180). （2022J180）
