Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

Applied Sciences

Yuanjie Zhi¹  Lingtong Min¹  Qinyi Lv¹  Deyun Zhou¹  Xiaoyang Li¹ 

[1] School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China;

关键词: domain adaptation;    fuzzy graph regularization;    sparse filtering;   

DOI  :  10.3390/app11104503

来源: DOAJ

【 摘 要 】

Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.

【 授权许可】

Unknown