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.

一种基于可解释的Takagi-Sugeno-Kang模糊系统的多视图模糊表示学习方法被提出，该方法从两个方面实现多视图表示学习：将多视图数据转化为高维模糊特征空间，并同时探索视图之间的共同信息和每个视图的特定信息；基于L_(2,1)-norm回归的新的正则化方法被提出以挖掘视图之间的一致性信息，并通过拉普拉斯图保持数据的几何结构；通过对多个基准多视图数据集进行广泛实验验证了该方法的优越性。

多视点模糊表示学习与基于规则模型