Multi-View Fuzzy Representation Learning With Rules Based Model

Unsupervised multi-view representation learning has been studied extensively 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 and ignore the specific information within each view. On the other hand, to mine the nonlinear relationship between the data, kernel or neural network methods are commonly used for multi-view representation learning but they lack 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. Extensive experiments on many benchmark multi-view datasets are conducted to validate the superiority of the proposed method.