Fuzzy graph learning regularized sparse filtering for visual domain adaptation

Lingtong Min, Deyun Zhou, Xiaoyang Li, Qinyi Lv, Yuanjie Zhi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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 crossdomain 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.

Original languageEnglish
Article number4503
JournalApplied Sciences (Switzerland)
Volume11
Issue number10
DOIs
StatePublished - 2 May 2021

Keywords

  • Domain adaptation
  • Fuzzy graph regularization
  • Sparse filtering

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