Search Results for author: Zhaohong Deng

Found 19 papers, 3 papers with code

Multi-view Fuzzy Representation Learning with Rules based Model

1 code implementation20 Sep 2023 Wei zhang, Zhaohong Deng, Te Zhang, Kup-Sze Choi, Shitong Wang

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.

Representation Learning

Multi-Label Takagi-Sugeno-Kang Fuzzy System

no code implementations20 Sep 2023 Qiongdan Lou, Zhaohong Deng, Zhiyong Xiao, Kup-Sze Choi, Shitong Wang

Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels.

Classification Multi-Label Classification

A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance

no code implementations9 Jan 2023 Qiongdan Lou, Zhaohong Deng, Kup-Sze Choi, Shitong Wang

Model transparency, label correlation learning and the robust-ness to label noise are crucial for multilabel learning.

Graph Fuzzy System: Concepts, Models and Algorithms

no code implementations30 Oct 2022 Fuping Hu, Zhaohong Deng, Zhenping Xie, Kup-Sze Choi, Shitong Wang

Finally, a learning framework of GFS is proposed, in which a kernel K-prototype graph clustering (K2PGC) is proposed to develop the construction algorithm for the GFS antecedent generation, and then based on graph neural network (GNNs), consequent parameters learning algorithm is proposed for GFS.

Graph Classification Graph Clustering

Dual Representation Learning for One-Step Clustering of Multi-View Data

1 code implementation30 Aug 2022 Wei zhang, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang

Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole.

Clustering Representation Learning

TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification

no code implementations8 Oct 2021 Wei zhang, Zhaohong Deng, Qiongdan Lou, Te Zhang, Kup-Sze Choi, Shitong Wang

The proposed method has the following distinctive characteristics: 1) it can deal with the incomplete and few labeled multi-view data simultaneously; 2) it integrates the missing view imputation and model learning as a single process, which is more efficient than the traditional two-step strategy; 3) attributed to the interpretable fuzzy inference rules, this method is more interpretable.


Double-Coupling Learning for Multi-Task Data Stream Classification

no code implementations15 Aug 2019 Yingzhong Shi, Zhaohong Deng, Haoran Chen, Kup-Sze Choi, Shitong Wang

Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream.

Classification General Classification

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

no code implementations12 Aug 2019 Zhaohong Deng, Chen Cui, Peng Xu, Ling Liang, Haoran Chen, Te Zhang, Shitong Wang

How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge.


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

no code implementations12 Aug 2019 Zhaohong Deng, Ruixiu Liu, Te Zhang, Peng Xu, Kup-Sze Choi, Bin Qin, Shitong Wang

The existing algorithms usually focus on the cooperation of different views in the original space but neglect the influence of the hidden information among these different visible views, or they only consider the hidden information between the views.


Deep Image Feature Learning with Fuzzy Rules

no code implementations25 May 2019 Xiang Ma, Liangzhe Chen, Zhaohong Deng, Peng Xu, Qisheng Yan, Kup-Sze Choi, Shitong Wang

The method progressively learns image features through a layer-by-layer manner based on fuzzy rules, so the feature learning process can be better explained by the generated rules.

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

1 code implementation25 May 2019 Peng Xu, Zhaohong Deng, Kup-Sze Choi, Longbing Cao, Shitong Wang

More specifically, it exploits the agreement and disagreement among views by sharing a common clustering results along the sample dimension and keeping the clustering results of each view specific along the feature dimension.


Joint Information Preservation for Heterogeneous Domain Adaptation

no code implementations22 May 2019 Peng Xu, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang

The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA).

Domain Adaptation

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

no code implementations24 Apr 2019 Peng Xu, Zhaohong Deng, Chen Cui, Te Zhang, Kup-Sze Choi, Gu Suhang, Jun Wang, Shitong Wang

Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS.

Clustering Sparse Learning

Transfer Representation Learning with TSK Fuzzy System

no code implementations9 Jan 2019 Peng Xu, Zhaohong Deng, Jun Wang, Qun Zhang, Shitong Wang

A core issue in transfer learning is to learn a shared feature space in where the distributions of the data from two domains are matched.

Dimensionality Reduction Representation Learning +1

Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views

no code implementations23 Jul 2018 Te Zhang, Zhaohong Deng, Dongrui Wu, Shitong Wang

Multi-view datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis.


Transfer Prototype-based Fuzzy Clustering

no code implementations19 Sep 2014 Zhaohong Deng, Yizhang Jiang, Fu-Lai Chung, Hisao Ishibuchi, Kup-Sze Choi, Shitong Wang

Specifically, the idea of leveraging knowledge from the source domain is exploited to develop a set of transfer prototype based fuzzy clustering (TPFC) algorithms.

Clustering Transfer Learning

A Survey on Soft Subspace Clustering

no code implementations19 Sep 2014 Zhaohong Deng, Kup-Sze Choi, Yizhang Jiang, Jun Wang, Shitong Wang

Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces.


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