K. PEARSON
Impact in
- Computational Mathematics top 0.05%
- Tensor decomposition and applications
-
- Matrix Theory and Algorithms
Papers in
-
- Tensor decomposition and applications 12
-
- Matrix Theory and Algorithms 7
- Co-authors
- Kai‐Wei Chang (4 shared papers)Tan Zhang (7 shared papers)Kung-Ching Chang (1 shared paper)April W. Armstrong (1 shared paper)
- Journals
- Communications in Mathematical Sciences (2 papers)Linear Algebra and its Applications (2 papers)Discrete Mathematics (2 papers)SIAM Journal on Matrix Analysis and Applications (1 paper)Journal of Mathematical Analysis and Applications (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
K. PEARSON
13 papers receiving 812 citations
Hit Papers
Peers
Comparison fields: 5 of 51
- Computational Mathematics 799
- Computational Theory and Mathematics 573
- Numerical Analysis 140
- Geometry and Topology 72
- Statistical and Nonlinear Physics 79
Countries citing papers authored by K. PEARSON
This map shows the geographic impact of K. PEARSON'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 K. PEARSON with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. PEARSON more than expected).
Fields of papers citing papers by K. PEARSON
This network shows the impact of papers produced by K. PEARSON. 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 K. PEARSON. The network helps show where K. PEARSON may publish in the future.
Co-authors
The 4 scholars most cited alongside K. PEARSON, 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 | Perron-Frobenius theorem for nonnegative tensors Hit paper breakdown → | 2008 | 354 |
| 2 | 2008 | 125 | |
| 3 | 2013 | 101 | |
| 4 | 2011 | 95 | |
| 5 | 2013 | 84 | |
| 6 | Essentially Positive Tensors | 2010 | 55 |
| 7 | 2015 | 11 | |
| 8 | 2016 | 8 | |
| 9 | 2014 | 5 | |
| 10 | 2013 | 4 | |
| 11 | 2018 | 3 | |
| 12 | 2012 | 3 | |
| 13 | 2012 | 3 |
About K. PEARSON
K. PEARSON is a scholar working on Computational Mathematics, Computational Theory and Mathematics, Numerical Analysis, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 13 papers that have together received 851 indexed citations. Recurring topics across this work include Tensor decomposition and applications (12 papers), Matrix Theory and Algorithms (7 papers), Advanced Neuroimaging Techniques and Applications (3 papers), Advanced Optimization Algorithms Research (3 papers), Psoriasis: Treatment and Pathogenesis (1 paper), Vitamin D Research Studies (1 paper), Retinoids in leukemia and cellular processes (1 paper) and Elasticity and Material Modeling (1 paper). The work is most often cited by research in Computational Mathematics (799 citations), Computational Theory and Mathematics (573 citations), Numerical Analysis (140 citations), Geometry and Topology (72 citations) and Statistical and Nonlinear Physics (79 citations). K. PEARSON has collaborated with scholars based in United States and China. Frequent co-authors include Kai‐Wei Chang, Tan Zhang, Kung-Ching Chang and April W. Armstrong. Their work appears in journals such as Communications in Mathematical Sciences, Linear Algebra and its Applications, Discrete Mathematics, SIAM Journal on Matrix Analysis and Applications and Journal of Mathematical Analysis and Applications.
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.