2026 Ivor W. Tsang: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	77	1249	1208	25	25	377	28885

Ivor W. Tsang publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Ivor W. Tsang sits on this spectrum.

32 publications 991+

This scientist: 377 publications — 85th percentile

85% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Ivor W. Tsang D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Ivor W. Tsang sits on this spectrum.

30 D-Index 131+

This scientist: 77 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

2025 - Research.com Computer Science in Australia Leader Award
2023 - Research.com Computer Science in Australia Leader Award
2022 - Research.com Computer Science in Australia Leader Award

Overview

Ivor W. Tsang is affiliated with the University of Technology Sydney in Australia. Their research primarily focuses on computer science, with extensive work in artificial intelligence, computer vision and pattern recognition, information systems, signal processing, and management science and operations research. Their scholarly output includes 415 publications in computer science, highlighting a broad engagement in this field.

Their research covers various subfields and topics, notably:

Artificial Intelligence
Computer Vision and Pattern Recognition
Information Systems
Signal Processing
Management Science and Operations Research

Key research topics associated with Ivor W. Tsang's work include:

Domain Adaptation and Few-Shot Learning
Topic Modeling
Advanced Graph Neural Networks
Machine Learning and Algorithms
Machine Learning and Data Classification
Adversarial Robustness in Machine Learning
Anomaly Detection Techniques and Applications

Their recent publications demonstrate contributions to graph learning, label-noise representation, graph attributes, imitation learning, and remote sensing. Some notable papers are:

"Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-View Clustering," 2021, published in IEEE Transactions on Knowledge and Data Engineering
"A Survey of Label-noise Representation Learning: Past, Present and Future," 2020, published in arXiv (Cornell University)
"Learning on Attribute-Missing Graphs," 2020, published in IEEE Transactions on Pattern Analysis and Machine Intelligence
"Imitation Learning: Progress, Taxonomies and Challenges," 2022, published in IEEE Transactions on Neural Networks and Learning Systems
"Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks," 2020, published in Remote Sensing

Ivor W. Tsang frequently publishes in several venues, including:

arXiv (Cornell University)
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Neural Networks and Learning Systems
Machine Learning

The scientist collaborates regularly with several coauthors. Frequent collaborators include Yuangang Pan, Bowen Xing, Yew-Soon Ong, Ya Zhang, and Xiaofeng Cao. These partnerships contribute to advancing various topics in the fields of artificial intelligence and machine learning.

Best Publications

Domain Adaptation via Transfer Component Analysis

Sinno Jialin Pan;Ivor W Tsang;James T Kwok;Qiang Yang

4734
Citations
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

Bo Han;Quanming Yao;Xingrui Yu;Gang Niu

1596
Citations
Core Vector Machines: Fast SVM Training on Very Large Data Sets

Ivor W. Tsang;James T. Kwok;Pak-Ming Cheung

1355
Citations
The pre-image problem in kernel methods

J.T.-Y. Kwok;I.W.-H. Tsang

1173
Citations
Visual event recognition in videos by learning from web data

Lixin Duan;Dong Xu;Ivor Wai-Hung Tsang;Jiebo Luo

624
Citations
Domain Transfer Multiple Kernel Learning

Lixin Duan;I. W. Tsang;Dong Xu

594
Citations
Local features are not lonely – Laplacian sparse coding for image classification

Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia;Peilin Zhao

571
Citations
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction

Feiping Nie;Dong Xu;Ivor Wai-Hung Tsang;Changshui Zhang

555
Citations
Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation

Wen Li;Lixin Duan;Dong Xu;Ivor W. Tsang

483
Citations
Kernel sparse representation for image classification and face recognition

Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia

454
Citations
Visual event recognition in videos by learning from web data

Unknown

438
Citations
How does disagreement help generalization against label corruption

Xingrui Yu;Bo Han;Jiangchao Yao;Gang Niu

429
Citations
Improved Nyström low-rank approximation and error analysis

Kai Zhang;Ivor W. Tsang;James T. Kwok

421
Citations
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia

418
Citations
Region-Based Saliency Detection and Its Application in Object Recognition

Zhixiang Ren;Shenghua Gao;Liang-Tien Chia;Ivor Wai-Hung Tsang

395
Citations
Domain adaptation from multiple sources via auxiliary classifiers

Lixin Duan;Ivor W. Tsang;Dong Xu;Tat-Seng Chua

393
Citations
Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach

Lixin Duan;Dong Xu;I. W. Tsang

379
Citations
Domain Transfer SVM for video concept detection

Lixin Duan;Ivor W Tsang;Dong Xu;Stephen J Maybank

344
Citations
Learning with Augmented Features for Heterogeneous Domain Adaptation

Lixin Duan;Dong Xu;Ivor W. Tsang

342
Citations
Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering

Feiping Nie;Zinan Zeng;I. W. Tsang;Dong Xu

338
Citations
Maximum Margin Clustering Made Practical

Kai Zhang;I.W. Tsang;J.T. Kwok

336
Citations

Frequent Co-Authors

James T. Kwok Hong Kong University of Science and Technology

Dong Xu University of Hong Kong

Yew-Soon Ong Nanyang Technological University

Mingkui Tan South China University of Technology

Joey Tianyi Zhou Agency for Science, Technology and Research

Masashi Sugiyama RIKEN

Lixin Duan University of Electronic Science and Technology of China

Sinno Jialin Pan Chinese University of Hong Kong

Ya Zhang Shanghai Jiao Tong University

Shenghua Gao ShanghaiTech University

External Links

Personal website of Ivor W. Tsang Google Scholar page

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