most citedConcise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

49 citations · 52 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2019

Multi-View Fuzzy Clustering with The Alternative Learning between Shared Hidden Space and Partition

Zhaohong Deng, Chen Cui, Peng Xu +4

As the multi-view data grows in the real world, multi-view clus-tering has become a prominent technique in data mining, pattern recognition, and machine learning. How to exploit th…

cs.LG2019

Multi-view Clustering with the Cooperation of Visible and Hidden Views

Zhaohong Deng, Ruixiu Liu, Te Zhang +4

Multi-view data are becoming common in real-world modeling tasks and many multi-view data clustering algorithms have thus been proposed. The existing algorithms usually focus on th…

cs.LG20193 cited

Multi-view Information-theoretic Co-clustering for Co-occurrence Data

Peng Xu, Zhaohong Deng, Kup-Sze Choi +2

Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements…

cs.LG2019

Joint Information Preservation for Heterogeneous Domain Adaptation

Peng Xu, Zhaohong Deng, Kup-Sze Choi +2

Domain adaptation aims to assist the modeling tasks of the target domain with knowledge of the source domain. The two domains often lie in different feature spaces due to diverse d…

cs.LG201949 cited

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

Peng Xu, Zhaohong Deng, Chen Cui +5

The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and ef…

cs.LG2019

Transfer Representation Learning with TSK Fuzzy System

Peng Xu, Zhaohong Deng, Jun Wang +2

Transfer learning can address the learning tasks of unlabeled data in the target domain by leveraging plenty of labeled data from a different but related source domain. A core issu…