Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation

@article{Luo2017RobustDG,
  title={Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation},
  author={Lingkun Luo and Xiaofang Wang and Shiqiang Hu and Liming Chen},
  journal={CoRR},
  year={2017},
  volume={abs/1705.08620}
}
Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unlabeled data in a target domain. In this paper, we propose a novel DA method, namely Robust Data Geometric Structure Aligned, Close yet Discriminative Domain Adaptation (RSA-CDDA), which brings closer, in a latent joint subspace, both source and target data distributions, and aligns inherent hidden source and target… CONTINUE READING
Related Discussions
This paper has been referenced on Twitter 8 times. VIEW TWEETS

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 18 references

Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation

IEEE Transactions on Image Processing • 2015
View 15 Excerpts
Highly Influenced

Transfer Feature Learning with Joint Distribution Adaptation

2013 IEEE International Conference on Computer Vision • 2013
View 9 Excerpts
Highly Influenced

Generalized Transfer Subspace Learning Through Low-Rank Constraint

International Journal of Computer Vision • 2014
View 11 Excerpts
Highly Influenced

Robust Recovery of Subspace Structures by Low-Rank Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2013
View 4 Excerpts
Highly Influenced

Robust visual domain adaptation with low-rank reconstruction

2012 IEEE Conference on Computer Vision and Pattern Recognition • 2012
View 4 Excerpts
Highly Influenced

Domain Adaptation via Transfer Component Analysis

IEEE Transactions on Neural Networks • 2003
View 6 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…