Search Results for author: Kup-Sze Choi

Found 15 papers, 4 papers with code

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.

Imputation MULTI-VIEW LEARNING

Network Together: Node Classification via Cross-Network Deep Network Embedding

1 code implementation4 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.

Domain Adaptation General Classification +2

Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation

no code implementations8 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.

CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images

no code implementations8 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.

Semantic Segmentation

Adversarial Deep Network Embedding for Cross-network Node Classification

2 code implementations18 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.

Classification Domain Adaptation +3

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 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, Zhaohong Deng, Peng Xu, Kup-Sze Choi, Dongrui Wu, 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.

Sparse Learning

Network Together: Node Classification via Cross network Deep Network Embedding

1 code implementation22 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

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.

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.

Transfer Learning

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