Unsupervised Visual Domain Adaptation Using Subspace Alignment

被引：998

作者：

Fernando, Basura ^{[1
]}

Habrard, Amaury ^{[2
]}

Sebban, Marc ^{[2
]}

Tuytelaars, Tinne ^{[1
]}

机构：

[1] Katholieke Univ Leuven, ESAT PSI, iMinds, Leuven, Belgium

[2] Lab Hubert Curien, UMR 5516, F-42000 St Etienne, France

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2013年

关键词：

D O I：

10.1109/ICCV.2013.368

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyperparameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.

引用

页码：2960 / 2967

页数：8