可解释性
人工智能
子空间拓扑
计算机科学
特征(语言学)
学习迁移
模糊逻辑
特征提取
推论
特征向量
模式识别(心理学)
机器学习
数据挖掘
语言学
哲学
作者
Qiongdan Lou,Wei Sun,Wei Zhang,Zhaohong Deng,Kup‐Sze Choi,Shitong Wang
出处
期刊:IEEE Transactions on Fuzzy Systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:32 (1): 306-321
标识
DOI:10.1109/tfuzz.2023.3298147
摘要
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, they still face 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: (i) 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 (ii) 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 (TSK-FS-HeFTL) 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.
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