Heterogeneous unsupervised domain adaptation based on fuzzy feature fusion

© 2017 IEEE. 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

Unconstrained fuzzy feature fusion for heterogeneous unsupervised domain adaptation

© 2018 IEEE. Domain adaptation can transfer knowledge from the source domain to improve pattern recognition accuracy in the target domain. However, it is rarely discussed when the target domain is unlabeled and heterogeneous with the source domain, which is a very challenging problem in the domain adaptation field. This paper presents a new feature reconstruction method: unconstrained fuzzy feature fusion. Through the reconstructed features of a source and a target domain, a geodesic flow kernel is applied to transfer knowledge between them. Furthermore, the original information of the target domain is also preserved when reconstructing the features of the two domains. Compared to the previous work, this work has two advantages: 1) the sum of the memberships of the original features to fuzzy features no longer must be one, and 2) the original information of the target domain is persevered. As a result of these advantages, this work delivers a better performance than previous studies using two public datasets

A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios

© 2019 IEEE. How to leverage knowledge from labelled domain (source) to help classify unlabeled domain (target) is a key problem in the machine learning field. Unsupervised domain adaptation (UDA) provides a solution to this problem and has been well developed for two homogeneous domains. However, when the target domain is unlabeled and heterogeneous with the source domain, current UDA models cannot accurately transfer knowledge from a source domain to a target domain. Benefiting from development of neural networks, this paper presents a new neural network, shared fuzzy equivalence relations neural network (SFER-NN), to address the heterogeneous UDA (HeUDA) problem. SFER-NN transfers knowledge across two domains according to shared fuzzy equivalence relations that can simultaneously cluster features of two domains into several categories. Based on the clustered categories, SFER-NN is constructed to minimize the discrepancy between two domains. Compared to previous works, SFER-NN is more capable of minimizing discrepancy between two domains. As a result of this advantage, SFER-NN delivers a better performance than previous studies using two public datasets