Cycle-reconstructive subspace learning with class discriminability for unsupervised domain adaptation

Highlights：

• Present an effective subspace learning approach for cross-domain image classification of unlabeled target objects.

• Two reconstructive matrixes are used through an iterative strategy to cycle-reconstruct data matrixes and update the common subspace.

• Different constraints are imposed on different reconstruction matrixes to preserve different structure information of the original domains.

• Considering the limitations of subspace learning, class discriminative constraints are added to improve recognition accuracy.

• Sufficient experiment results show that our proposed traditional method outperforms state-of-the-art traditional methods and is comparable with advanced deep methods on five current domain adaptation datasets.