Rules-Based Heterogeneous Feature Transfer Learning Using Fuzzy Inference

Heterogeneous feature transfer (HeFT) learning can leverage the semantically related source domain from a different feature space for modeling the target domain with insufficient information. Although HeFT learning has made significant progress, it still faces two major challenges: weak interpretability of the transfer process and underutilization of the hidden information of the heterogeneous source and target domains. To address these two challenges, a framework called heterogeneous feature transfer using fuzzy inference rules (HeFT-FIR) is proposed. The HeFT-FIR framework has two parts: First, design of Takagi–Sugeno–Kang fuzzy systems (TSK-FSs) for the source and target domains, respectively, to achieve HeFT and enhance the interpretability of the transfer process; and second, integration of the HeFT learning mechanism with fuzzy inference rules to optimize the parameters of TSK-FSs and mine the hidden information of the two domains. Based on the framework, a TSK-FS-based heterogeneous feature transfer learning method is then developed with three fuzzy feature space-based learning mechanisms for joint distribution adaptation, local geometric property preservation, and heterogeneous discriminant information extraction, respectively. The mechanisms reduce the difference in distribution between the heterogeneous source and target domains in a common feature subspace, preserve the local geometric properties of two domains, and extract the global discriminant information of them. Extensive analyses are conducted to verify the superiority of the proposed framework and method.