Quantum subspace alignment for domain adaptation

@article{He2020QuantumSA,
  title={Quantum subspace alignment for domain adaptation},
  author={Xi He and Xiaoting Wang},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.02472}
}
Domain adaptation (DA) is used for adaptively obtaining labels of an unprocessed data set with a given related, but different labelled data set. Subspace alignment (SA), a representative DA algorithm, attempts to find a linear transformation to align the subspaces of the two different data sets. The classifier trained on the aligned labelled data set can be transferred to the unlabelled data set to predict the target labels. In this paper, two quantum versions of the SA are proposed to… 
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