Lek‐Heng Lim
- Computational Mathematics top 0.01%
- Computational Theory and Mathematics top 0.5%
- Computational Mechanics top 1%
- Artificial Intelligence top 2%
- Signal Processing top 2%
- Co-authors
- Christopher J. HillarPierre ComonKe YeGene H. GolubBernard MourrainDavid F. GleichBerkant SavasXiaoye Jiang
- Topics
- Tensor decomposition and applications (25 papers)Matrix Theory and Algorithms (20 papers)Sparse and Compressive Sensing Techniques (8 papers)
- Partner nations
- United StatesChinaFrance
In The Last Decade
Lek‐Heng Lim
48 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Computational Mathematics 2.1k
- Computational Theory and Mathematics 1.1k
- Computational Mechanics 849
- Artificial Intelligence 496
- Signal Processing 415
Countries citing papers authored by Lek‐Heng Lim
This map shows the geographic impact of Lek‐Heng Lim'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 Lek‐Heng Lim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lek‐Heng Lim more than expected).
Fields of papers citing papers by Lek‐Heng Lim
This network shows the impact of papers produced by Lek‐Heng Lim. 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 Lek‐Heng Lim. The network helps show where Lek‐Heng Lim may publish in the future.
Co-authorship network of co-authors of Lek‐Heng Lim
This figure shows the co-authorship network connecting the top 25 collaborators of Lek‐Heng Lim. A scholar is included among the top collaborators of Lek‐Heng Lim 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 Lek‐Heng Lim. Lek‐Heng Lim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 106 | |
| 6 | Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False | 1 |
| 7 | 6 | |
| 8 | 7 | |
| 9 | 7 | |
| 10 | Complex tensors almost always have best low-rank approximations | 1 |
| 11 | 12 | |
| 12 | 7 | |
| 13 | Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top | 3 |
| 14 | 55 | |
| 15 | 177 | |
| 16 | 65 | |
| 17 | Learning to rank with combinatorial Hodge theory | 4 |
| 18 | 64 | |
| 19 | 7 | |
| 20 | Foundations of numerical multilinear algebra: decomposition and approximation of tensors | 17 |
About Lek‐Heng Lim
Lek‐Heng Lim is a scholar working on Computational Mathematics, Computational Theory and Mathematics and Numerical Analysis, having authored 52 papers that have together received 3.1k indexed citations. Recurring topics across this work include Tensor decomposition and applications (25 papers), Matrix Theory and Algorithms (20 papers) and Sparse and Compressive Sensing Techniques (8 papers). The work is most often cited by research in Computational Mathematics (2.1k citations), Computational Theory and Mathematics (1.1k citations) and Numerical Analysis (267 citations). Lek‐Heng Lim has collaborated with scholars based in United States, China and France. Frequent co-authors include Christopher J. Hillar, Pierre Comon, Ke Ye, Gene H. Golub, Bernard Mourrain, David F. Gleich, Berkant Savas, Xiaoye Jiang, Yinyu Ye and Yuan Yao. Their work appears in journals such as NeuroImage, IEEE Transactions on Information Theory and Journal of the ACM.
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.