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.

翻译：无监督多视图表示学习已被广泛用于挖掘多视图数据，但仍存在若干关键挑战。一方面，现有方法通常仅学习视图间的公共表示，而多视图数据同时包含视图间的公共信息与各视图的特定信息，因此无法全面探索数据特征。另一方面，为挖掘数据间的非线性关系，核方法或神经网络常被用于多视图表示学习，但这些方法缺乏可解释性。为此，本文提出一种基于可解释的Takagi-Sugeno-Kang（TSK）模糊系统的新型多视图模糊表示学习方法（MVRL_FS）。该方法从两个方面实现多视图表示学习：首先，将多视图数据映射到高维模糊特征空间，同时挖掘视图间的公共信息与各视图的特定信息；其次，提出一种基于L_(2,1)-范数回归的新型正则化方法，在挖掘视图间一致信息的同时，通过拉普拉斯图保留数据的几何结构。最后，在多个基准多视图数据集上的广泛实验验证了所提方法的优越性。