南京理工大学学报（自然科学版）2026，Vol.50Issue(3)：283-294,12.DOI:10.14177/j.cnki.32-1397n.2026.50.03.005
基于低秩重构的迁移子空间学习物体图像识别方法
Low-rank reconstruction based transfer subspace learning method for object image recognition
摘要
Abstract
Transfer subspace learning based on low-rank reconstruction is an effective cross-domain learning method,but most of existing methods only consider the classification interval between categories and lose some discriminative information,and are prone to overfitting.To address these issues and enhance the classification performance of the model,this paper proposes a low-rank reconstruction based transfer subspace learning(LRR-TSL)method.On the basis of transferring subspaces,low-rank constraints are used to ensure alignment between the source and target domains within the subspace,and sparse constraints are used to ensure reconstruction from the source domain to the target domain.A relaxed label matrix is introduced and combined with the ε-draggings technique to improve the intra-class compactness and inter-class separation of the model,while avoiding overfitting of the relaxed labels.The linear entropy based on the reconstruction matrix and the label matrix is constructed to further enhance the discriminative ability of the model.Cross-domain image recognition experiments on Office and Caltech-256 datasets demonstrate the effectiveness of the proposed method in this paper.关键词
迁移子空间学习/低秩重构/线性熵/分类Key words
transfer subspace learning/low-rank reconstruction/linear entropy/classification分类
信息技术与安全科学引用本文复制引用
过林吉,金艳云..基于低秩重构的迁移子空间学习物体图像识别方法[J].南京理工大学学报（自然科学版）,2026,50(3):283-294,12.基金项目
国家自然科学基金(12005182) （12005182）
