M.H.C. Law

2.4k total citations
13 papers, 1.3k citations indexed

About

M.H.C. Law is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, M.H.C. Law has authored 13 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 3 papers in Signal Processing. Recurrent topics in M.H.C. Law's work include Advanced Clustering Algorithms Research (7 papers), Face and Expression Recognition (6 papers) and Bayesian Methods and Mixture Models (5 papers). M.H.C. Law is often cited by papers focused on Advanced Clustering Algorithms Research (7 papers), Face and Expression Recognition (6 papers) and Bayesian Methods and Mixture Models (5 papers). M.H.C. Law collaborates with scholars based in United States, Portugal and Hong Kong. M.H.C. Law's 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 and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Decision Support Systems and Neural Information Processing Systems.

In The Last Decade

M.H.C. Law

13 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M.H.C. Law United States 12 794 570 210 201 121 13 1.3k
Mohamed Nadif France 20 903 1.1× 329 0.6× 181 0.9× 185 0.9× 190 1.6× 84 1.3k
Srujana Merugu United States 14 1.1k 1.4× 477 0.8× 261 1.2× 481 2.4× 74 0.6× 27 1.8k
Guy Lebanon United States 21 667 0.8× 394 0.7× 156 0.7× 398 2.0× 46 0.4× 56 1.2k
Alexander L. Strehl United States 20 1.3k 1.7× 318 0.6× 176 0.8× 262 1.3× 84 0.7× 28 1.8k
Ayhan Demiriz Türkiye 11 888 1.1× 550 1.0× 148 0.7× 168 0.8× 63 0.5× 30 1.4k
Subramanyam Mallela United States 8 1.0k 1.3× 477 0.8× 245 1.2× 451 2.2× 184 1.5× 9 1.5k
Claudio Gentile Italy 20 1.4k 1.7× 306 0.5× 145 0.7× 246 1.2× 63 0.5× 66 1.8k
Maria-Florina Balcan United States 23 1.0k 1.3× 271 0.5× 131 0.6× 87 0.4× 40 0.3× 86 1.6k
Stephen M. Chu United States 20 582 0.7× 436 0.8× 372 1.8× 120 0.6× 54 0.4× 56 1.2k
Joseph Turian United States 7 1.7k 2.1× 444 0.8× 169 0.8× 181 0.9× 178 1.5× 11 2.2k

Countries citing papers authored by M.H.C. Law

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of M.H.C. Law

This figure shows the co-authorship network connecting the top 25 collaborators of M.H.C. Law. A scholar is included among the top collaborators of M.H.C. Law 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 M.H.C. Law. M.H.C. Law is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Law, M.H.C. & Anil K. Jain. (2006). Incremental nonlinear dimensionality reduction by manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(3). 377–391. 189 indexed citations
2.
Topchy, A., M.H.C. Law, Anil K. Jain, & Ana Fred. (2005). Analysis of Consensus Partition in Cluster Ensemble. 225–232. 110 indexed citations
3.
Lange, Tilman, M.H.C. Law, Anil K. Jain, & Joachim M. Buhmann. (2005). Learning with Constrained and Unlabelled Data. 1. 731–738. 60 indexed citations
4.
Law, M.H.C., A. Topchy, & Anil K. Jain. (2005). Model-based Clustering With Probabilistic Constraints. 641–645. 38 indexed citations
5.
Law, M.H.C., A. Topchy, & Anil K. Jain. (2004). Multiobjective data clustering. 2. 424–430. 125 indexed citations
6.
Law, M.H.C., Mário A. T. Figueiredo, & Anil K. Jain. (2004). Simultaneous feature selection and clustering using mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(9). 1154–1166. 439 indexed citations
7.
Jain, Anil K., A. Topchy, M.H.C. Law, & Joachim M. Buhmann. (2004). Landscape of clustering algorithms. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.. 260–263 Vol.1. 52 indexed citations
8.
Law, M.H.C., Nan Zhang, & Anil K. Jain. (2004). Nonlinear Manifold Learning For Data Stream. 33–44. 26 indexed citations
9.
Cheung, William K., et al.. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems. 35(2). 231–243. 179 indexed citations
10.
Law, M.H.C., Anil K. Jain, & Mário A. T. Figueiredo. (2002). Feature Selection in Mixture-Based Clustering. Neural Information Processing Systems. 641–648. 49 indexed citations
11.
Law, M.H.C. & James T. Kwok. (2002). Rival penalized competitive learning for model-based sequence clustering. 2. 195–198. 19 indexed citations
12.
Law, M.H.C. & James T. Kwok. (2001). Bayesian support vector regression. International Conference on Artificial Intelligence and Statistics. 110(Pt 6). 162–167. 27 indexed citations
13.

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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