Abstract
Effectively extracting discriminative information from multi-view data remains a key challenge in multi-view learning. Existing methods typically focus on exploring inter-view consistency via linear or nonlinear transformation. While nonlinear methods tend to yield better performance, their limited transparency and interpretability limit their practical applications. Takagi-Sugeno-Kang Fuzzy Systems (TSK-FS), as a rule-based model with high interpretability, have been applied to multi-view tasks. However, prior methods either rely solely on antecedent components for nonlinear modeling or integrate deep neural networks into the consequent part, thereby compromising model interpretability. To address these challenges, we propose Fuzzy Rule-guided Multi-view Differentiable Representation Learning (FRMVDRL). Specifically, in our framework, antecedent parameters of TSK-FS are first used to map data into highdimensional fuzzy space. Then during the learning of consequent parameters, a dual information extraction mechanism is proposed to jointly capture shared knowledge across views and view-specific knowledge. Moreover, a second-order geometric structure preservation mechanism is constructed to exploit structural information at both the instance and the instance-pair level. To enhance discriminability of the learned representations, a biorthogonal constraint alongside a Shannon entropy mechanism is introduced. Finally, to balance model performance and interpretability, we introduce a novel multi-view differentiable optimization strategy that incorporates learnable parameters to expand the solution space while preserving the structure of traditional optimization. Extensive experiments on benchmark multi-view datasets demonstrate the effectiveness of the FRMVDRL.