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Quantum classifiers for domain adaptation
Xi He, Feiyu Du, Mingyuan Xue +3
Transfer learning (TL), a crucial subfield of machine learning, aims to accomplish a task in the target domain with the acquired knowledge of the source domain. Specifically, effec…
Quantum transfer component analysis for domain adaptation
Xi He, Chufan Lyu, Min-Hsiu Hsieh +1
Domain adaptation, a crucial sub-field of transfer learning, aims to utilize known knowledge of one data set to accomplish tasks on another data set. In this paper, we perform one…
Quantum subspace alignment for domain adaptation
Xi He
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 represen…
Quantum locally linear embedding for nonlinear dimensionality reduction
Xi He, Li Sun, Chufan Lyu +1
Reducing the dimension of nonlinear data is crucial in data processing and visualization. The locally linear embedding algorithm (LLE) is specifically a representative nonlinear di…