In this paper, a novel Takagi-Sugeno-Kang (TSK) fuzzy classification system (FCS) is firstly presented for pattern classification tasks. It is distinguished by having the large margin criterion properly integrated into its objective function. In order to exploit the applicability of fuzzy systems in multiview scenarios, the proposed TSK-FCS is extended to a two-view version, called two-view TSK-FCS (TwoV-TSK-FCS), by using a collaborative learning mechanism. The adopted collaborative learning mechanism not only fully considers the independent information of each view, but also effectively discovers the correlation information hidden in the two views. Thus, the performance of TwoV-TSK-FCS can be enhanced accordingly. Comprehensive experiments on two-view synthetic and UCI datasets demonstrate the effectiveness of the proposed two-view FCS.

The success of fuzzy clustering heavily relies on the features of the input data. Based on the fact that deep architectures are able to more accurately characterize the data representations in a layer-by-layer manner, this paper proposes a novel feature mapping technique called cascaded hidden-space (CHS) feature mapping and investigates its combination with classical fuzzy c-means (FCM) and fuzzy c-regressions (FCR). Since the parameters between the layers of CHS feature mapping are randomly generated and need not be tuned layer-by-layer, CHS is easily implemented with less training data. By performing classical FCM in CHS, a novel fuzzy clustering framework called CHS-FCM is developed; several of its variants are presented using different dimension-reduction methods in a CHS-FCM clustering framework. The combination of CHS-FCM with nonlinear switch regressions is called CHS-FCR, and it performs FCR in CHS. The proposed CHS-FCR provides better results than FCR for nonlinear process modeling. Both CHS-FCM and CHS-FCR exhibit low memory consumption and require less training data. The experimental results verify the superiority of the proposed methods over classical fuzzy clustering methods.

In this paper, to effectively use the decision knowledge from multiple source domains to predict the labels of samples in the target domain, a novel doubly reweighting multisource transfer learning called DRMTL framework is proposed. DRMTL aims to simultaneously optimize the structural risk function, domain reweighting adaptation, pointwise reweighting adaptation and manifold consistency. The merits of DRMTL include the following: 1) The importance of every source domain can be evaluated using the proposed novel flexible weighting index; 2) The loss between an unknown label prediction and its prediction by some source decision function for a target sample can be reweighted using a novel domain separator; and 3) The manifold structure of the target domain is effectively used in this framework. Finally, a specific learning algorithm, i.e., a doubly reweighting multisource transfer learning using the regularized least-squares classifier called DRM-RLS, is proposed using the DRMTL framework and the classical regularized least-squares classifier, and its convergence is also proven. Our experimental results from several real-world datasets reveal that the proposed approach outperforms several state-of-the-art transfer learning algorithms.