M.H.C. Law
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- Face and Expression Recognition 6
- Image Retrieval and Classification Techniques 2
- Artificial Intelligence top 2%
- Advanced Clustering Algorithms Research 7
- Bayesian Methods and Mixture Models 5
- Neural Networks and Applications 3
- Machine Learning and Algorithms 1
- Signal Processing top 5%
- Data Management and Algorithms 3
- Information Systems top 5%
- Recommender Systems and Techniques 2
- Media Technology top 5%
- Co-authors
- Anil K. JainMário A. T. FigueiredoA. TopchyJames T. KwokWilliam K. CheungAna FredJoachim M. BuhmannTilman Lange
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)Decision Support Systems (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesPortugalHong Kong
In The Last Decade
M.H.C. Law
13 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 116
- Computer Vision and Pattern Recognition 570
- Artificial Intelligence 794
- Signal Processing 210
- Information Systems 201
- Media Technology 75
Countries citing papers authored by M.H.C. Law
This map shows the geographic impact of M.H.C. Law'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 M.H.C. Law with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M.H.C. Law more than expected).
Fields of papers citing papers by M.H.C. Law
This network shows the impact of papers produced by M.H.C. Law. 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 M.H.C. Law. The network helps show where M.H.C. Law may publish in the future.
Co-authorship network
The 9 scholars most cited alongside M.H.C. Law, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 189 | |
| 2 | 2005 | 110 | |
| 3 | 2005 | 60 | |
| 4 | 2005 | 38 | |
| 5 | 2004 | 125 | |
| 6 | 2004 | 439 | |
| 7 | 2004 | 52 | |
| 8 | 2004 | 26 | |
| 9 | 2003 | 179 | |
| 10 | Feature Selection in Mixture-Based Clustering | 2002 | 49 |
| 11 | 2002 | 19 | |
| 12 | 2001 | 27 | |
| 13 | 2000 | 4 |
About M.H.C. Law
M.H.C. Law is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 13 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (7 papers), Face and Expression Recognition (6 papers), Bayesian Methods and Mixture Models (5 papers), Neural Networks and Applications (3 papers), Data Management and Algorithms (3 papers), Recommender Systems and Techniques (2 papers), Image Retrieval and Classification Techniques (2 papers) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (570 citations), Artificial Intelligence (794 citations) and Signal Processing (210 citations). M.H.C. Law has collaborated with scholars based in United States, Portugal and Hong Kong. Frequent co-authors include Anil K. Jain, Mário A. T. Figueiredo, A. Topchy, James T. Kwok, William K. Cheung, Ana Fred, Joachim M. Buhmann, Tilman Lange and Nan Zhang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Decision Support Systems and Neural Information Processing Systems.
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.