Heterogeneous unsupervised domain adaptation based on fuzzy feature fusion

Domain adaptation is a transfer learning approach that has been widely studied in the last decade. However, existing works still have two limitations: 1) the feature spaces of the domains are homogeneous, and 2) the target domain has at least a few labeled instances. Both limitations significantly restrict the domain adaptation approach when knowledge is transferred across domains, especially in the current era of big data. To address both issues, this paper proposes a novel fuzzy-based heterogeneous unsupervised domain adaptation approach. This approach maps the feature spaces of the source and target domains onto the same latent space constructed by fuzzy features. In the new feature space, the label spaces of two domains are maintained to reduce the probability of negative transfer occurring. The proposed approach delivers superior performance over current benchmarks, and the heterogeneous unsupervised domain adaptation (HeUDA) method provides a promising means of giving a learning system the associative ability to judge unknown things using related knowledge.