Lek‐Heng Lim
- Computational Mathematics top 0.01%
- Tensor decomposition and applications 25
- Computational Theory and Mathematics top 0.5%
- Matrix Theory and Algorithms 20
- Topological and Geometric Data Analysis 4
- Numerical Analysis top 2%
- Advanced Optimization Algorithms Research 7
- Computational Mechanics top 1%
- Sparse and Compressive Sensing Techniques 8
- Signal Processing top 2%
- Blind Source Separation Techniques 8
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- Advanced Statistical Methods and Models 3
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- Mathematics and Applications 3
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
- Numerical Analysis 267
- Computational Mechanics 849
- 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
The 25 scholars most cited alongside Lek‐Heng Lim, 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 | 2025 | 0 | |
| 2 | 2023 | 7 | |
| 3 | 2023 | 2 | |
| 4 | 2021 | 0 | |
| 5 | 2020 | 106 | |
| 6 | Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False | 2020 | 1 |
| 7 | 2020 | 6 | |
| 8 | 2019 | 7 | |
| 9 | 2019 | 7 | |
| 10 | Complex tensors almost always have best low-rank approximations | 2017 | 1 |
| 11 | 2017 | 12 | |
| 12 | 2017 | 7 | |
| 13 | Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top | 2015 | 3 |
| 14 | 2014 | 55 | |
| 15 | 2010 | 177 | |
| 16 | 2010 | 65 | |
| 17 | Learning to rank with combinatorial Hodge theory | 2008 | 4 |
| 18 | 2008 | 64 | |
| 19 | 2008 | 7 | |
| 20 | Foundations of numerical multilinear algebra: decomposition and approximation of tensors | 2007 | 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), Sparse and Compressive Sensing Techniques (8 papers), Blind Source Separation Techniques (8 papers), Advanced Optimization Algorithms Research (7 papers), Topological and Geometric Data Analysis (4 papers), Advanced Statistical Methods and Models (3 papers) and Mathematics and Applications (3 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.