1 code implementation • 20 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.
2 code implementations • 20 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.
1 code implementation • 31 Aug 2023 • Xiao Shen, Shirui Pan, Kup-Sze Choi, Xi Zhou
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently.
no code implementations • 17 Jan 2023 • Yuanyuan Wang, Zhaohong Deng, Qiongdan Lou, Shudong Hu, Kup-Sze Choi, Shitong Wang
Secondly, a fusion module is designed to integrate the features from two branches.
no code implementations • 9 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.
1 code implementation • 30 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.
1 code implementation • 30 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.
no code implementations • 11 Nov 2021 • Andong Li, Zhaohong Deng, Qiongdan Lou, Kup-Sze Choi, Hongbin Shen, Shitong Wang
In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy.
no code implementations • 8 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.
1 code implementation • 4 Jun 2020 • Xiao Shen, Quanyu Dai, Sitong Mao, Fu-Lai Chung, Kup-Sze Choi
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
no code implementations • 8 Apr 2020 • Youyi Song, Zhen Yu, Teng Zhou, Jeremy Yuen-Chun Teoh, Baiying Lei, Kup-Sze Choi, Jing Qin
Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose.
no code implementations • 8 Apr 2020 • Youyi Song, Lei Zhu, Baiying Lei, Bin Sheng, Qi Dou, Jing Qin, Kup-Sze Choi
In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump.
2 code implementations • 18 Feb 2020 • Xiao Shen, Quanyu Dai, Fu-Lai Chung, Wei Lu, Kup-Sze Choi
This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information.
no code implementations • 15 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.
no code implementations • 12 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.
no code implementations • 25 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.
1 code implementation • 25 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.
no code implementations • 22 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).
no code implementations • 24 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.
1 code implementation • 22 Jan 2019 • Xiao Shen, Quanyu Dai, Sitong Mao, Fu-Lai Chung, Kup-Sze Choi
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Social and Information Networks
no code implementations • 19 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.
no code implementations • 19 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.