Pak-Ming Cheung

1.2k total citations · 1 hit paper
5 papers, 819 citations indexed

About

Pak-Ming Cheung is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Pak-Ming Cheung has authored 5 papers receiving a total of 819 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Molecular Biology. Recurrent topics in Pak-Ming Cheung's work include Advanced Image and Video Retrieval Techniques (3 papers), Image Retrieval and Classification Techniques (3 papers) and Human Pose and Action Recognition (2 papers). Pak-Ming Cheung is often cited by papers focused on Advanced Image and Video Retrieval Techniques (3 papers), Image Retrieval and Classification Techniques (3 papers) and Human Pose and Action Recognition (2 papers). Pak-Ming Cheung collaborates with scholars based in Hong Kong. Pak-Ming Cheung's co-authors include James T. Kwok and Ivor W. Tsang and has published in prestigious journals such as Journal of Machine Learning Research, Rare & Special e-Zone (The Hong Kong University of Science and Technology) and Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

In The Last Decade

Pak-Ming Cheung

5 papers receiving 768 citations

Hit Papers

Core Vector Machines: Fast SVM Training on Very Large Dat... 2005 2026 2012 2019 2005 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Core Vector Machines: Fast SVM Training on Very Large Data Sets
    2005 657 citations Ivor W. Tsang, James T. Kwok et al. Journal of Machine Learning Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pak-Ming Cheung Hong Kong 5 527 456 90 71 55 5 819
Mingrui Wu United States 14 541 1.0× 526 1.2× 86 1.0× 76 1.1× 49 0.9× 24 917
Terry Windeatt United Kingdom 19 448 0.9× 445 1.0× 42 0.5× 83 1.2× 36 0.7× 56 934
Yanshan Xiao China 17 616 1.2× 511 1.1× 107 1.2× 73 1.0× 89 1.6× 100 1.0k
Hong-Jie Xing China 13 391 0.7× 169 0.4× 94 1.0× 74 1.0× 58 1.1× 42 640
Dit-Yan Yeung Hong Kong 10 648 1.2× 469 1.0× 125 1.4× 136 1.9× 125 2.3× 14 1.1k
S. Sundararajan India 6 569 1.1× 385 0.8× 68 0.8× 51 0.7× 39 0.7× 17 851
Grigorios Tzortzis Greece 9 425 0.8× 386 0.8× 31 0.3× 86 1.2× 62 1.1× 11 717
Hamidreza Rashidy Kanan Iran 17 314 0.6× 521 1.1× 59 0.7× 131 1.8× 32 0.6× 56 853
Simon Günter Switzerland 12 386 0.7× 309 0.7× 40 0.4× 114 1.6× 39 0.7× 18 712
Michael Mavroforakis Greece 11 366 0.7× 306 0.7× 63 0.7× 52 0.7× 20 0.4× 15 674

Countries citing papers authored by Pak-Ming Cheung

Since Specialization
Citations

This map shows the geographic impact of Pak-Ming Cheung's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pak-Ming Cheung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pak-Ming Cheung more than expected).

Fields of papers citing papers by Pak-Ming Cheung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pak-Ming Cheung. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pak-Ming Cheung. The network helps show where Pak-Ming Cheung may publish in the future.

Co-authorship network of co-authors of Pak-Ming Cheung

This figure shows the co-authorship network connecting the top 25 collaborators of Pak-Ming Cheung. A scholar is included among the top collaborators of Pak-Ming Cheung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pak-Ming Cheung. Pak-Ming Cheung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
1.
Kwok, James T. & Pak-Ming Cheung. (2007). Marginalized multi-instance kernels. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 901–906. 43 indexed citations
2.
Cheung, Pak-Ming & James T. Kwok. (2006). A regularization framework for multiple-instance learning. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 193–200. 64 indexed citations
3.
Tsang, Ivor W., Pak-Ming Cheung, & James T. Kwok. (2006). Kernel relevant component analysis for distance metric learning. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.. 2. 954–959. 32 indexed citations
4.
Tsang, Ivor W., James T. Kwok, & Pak-Ming Cheung. (2005). Core Vector Machines: Fast SVM Training on Very Large Data Sets. Journal of Machine Learning Research. 6(13). 363–392. 657 indexed citations breakdown →
5.
Tsang, Ivor W., James T. Kwok, & Pak-Ming Cheung. (2005). Very Large SVM Training using Core Vector Machines.. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 23 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026