Anh Huy Phan
- Computational Mathematics top 0.01%
- Signal Processing top 0.2%
- Computational Mechanics top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 1%
- Co-authors
- Andrzej CichockiRafał ZdunekШун-ичи АмариDanilo P. MandicQibin ZhaoCésar F. CaiafaPetr TichavskýLieven De Lathauwer
- Topics
- Tensor decomposition and applications (58 papers)Blind Source Separation Techniques (26 papers)Sparse and Compressive Sensing Techniques (15 papers)
In The Last Decade
Anh Huy Phan
95 papers receiving 5.2k citations
Hit Papers
Peers
Comparison fields: 5 of 152
- Computational Mathematics 2.4k
- Signal Processing 1.4k
- Computational Mechanics 1.2k
- Computer Vision and Pattern Recognition 863
- Artificial Intelligence 845
Countries citing papers authored by Anh Huy Phan
This map shows the geographic impact of Anh Huy Phan'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 Anh Huy Phan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anh Huy Phan more than expected).
Fields of papers citing papers by Anh Huy Phan
This network shows the impact of papers produced by Anh Huy Phan. 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 Anh Huy Phan. The network helps show where Anh Huy Phan may publish in the future.
Co-authorship network of co-authors of Anh Huy Phan
This figure shows the co-authorship network connecting the top 25 collaborators of Anh Huy Phan. A scholar is included among the top collaborators of Anh Huy Phan 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 Anh Huy Phan. Anh Huy Phan 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 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 9 | |
| 9 | 1 | |
| 10 | 4 | |
| 11 | 4 | |
| 12 | 4 | |
| 13 | Tensor diagonalization - a new tool for PARAFAC and block-term decomposition. | 4 |
| 14 | 11 | |
| 15 | 7 | |
| 16 | A treatment of EEG data by underdetermined blind source separation for motor imagery classification | 5 |
| 17 | 13 | |
| 18 | Damped Newton Iterations for Nonnegative Matrix Factorization | 2 |
| 19 | 3 | |
| 20 | 9 |
About Anh Huy Phan
Anh Huy Phan is a scholar working on Computational Mathematics, Signal Processing and Computational Mechanics, having authored 100 papers that have together received 5.4k indexed citations. Recurring topics across this work include Tensor decomposition and applications (58 papers), Blind Source Separation Techniques (26 papers) and Sparse and Compressive Sensing Techniques (15 papers). The work is most often cited by research in Computational Mathematics (2.4k citations), Signal Processing (1.4k citations) and Computational Mechanics (1.2k citations). Anh Huy Phan has collaborated with scholars based in Japan, Czechia and Russia. Frequent co-authors include Andrzej Cichocki, Rafał Zdunek, Шун-ичи Амари, Danilo P. Mandic, Qibin Zhao, César F. Caiafa, Petr Tichavský, Lieven De Lathauwer, Guoxu Zhou and Roger W. Parish. Their work appears in journals such as PLoS ONE, The Plant Cell and IEEE Transactions on Signal Processing.
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.