Multi-view Fuzzy Representation Learning with Rules based Model

TL;DR

This paper introduces a novel multi-view fuzzy representation learning method using an interpretable TSK fuzzy system, effectively capturing both common and specific information across views while maintaining interpretability.

Contribution

It proposes a new multi-view fuzzy learning approach that explores shared and view-specific features in an interpretable fuzzy framework, addressing limitations of existing methods.

Findings

Outperforms existing methods on benchmark datasets.

Effectively captures both common and specific multi-view information.

Maintains interpretability through fuzzy rule-based modeling.

Abstract

Unsupervised multi-view representation learning has been extensively studied for mining multi-view data. However, some critical challenges remain. On the one hand, the existing methods cannot explore multi-view data comprehensively since they usually learn a common representation between views, given that multi-view data contains both the common information between views and the specific information within each view. On the other hand, to mine the nonlinear relationship between data, kernel or neural network methods are commonly used for multi-view representation learning. However, these methods are lacking in interpretability. To this end, this paper proposes a new multi-view fuzzy representation learning method based on the interpretable Takagi-Sugeno-Kang (TSK) fuzzy system (MVRL_FS). The method realizes multi-view representation learning from two aspects. First, multi-view data are transformed into a high-dimensional fuzzy feature space, while the common information between views and specific information of each view are explored simultaneously. Second, a new regularization method based on L_(2,1)-norm regression is proposed to mine the consistency information between views, while the geometric structure of the data is preserved through the Laplacian graph. Finally, extensive experiments on many benchmark multi-view datasets are conducted to validate the superiority of the proposed method.