Suhang Gu

TL;DR: An enhanced soft subspace clustering (ESSC) and sparse learning (SL) based concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of E SSC and SL.

TL;DR: A novel deep fuzzy classifier is devised by stacking a series of TSKa sub-classifiers and training them by a deep learning strategy, which has the potential feasibility of leveraging their smooth gradient information with respect to the inputs in the training input space to construct a stacked-structure-based deep fuzzyclassifier.

TL;DR: A novel unified classification framework based on the proposed fuzzy social network and its dynamics about fuzzy influences of nodes that demonstrates distinctive superiority on five case studies where satisfactory classification certainly depends on both physical and implicit style features.

TL;DR: A fuzzy style k-plane clustering algorithm is proposed to have its distinctive merits: first, the nuances between styles of clusters can be well identified by using the proposed twofold data representation, and by means of alternating optimization strategy, the objective function of S-KPC can be optimized such that each discriminant function of each cluster shares the advantages of both simple regression models and functional-link neural networks.

TL;DR: Zhang et al. as discussed by the authors proposed a multi-view affinity propagation (AP)-based clustering method with only one random initialization and one parameter, which can improve the consensus of exemplars across different views.