Tensor decomposition and applications
Tensor decomposition and applications is a research topic within Computational Mathematics. Science Explorer counts 7.4k research works in it since 1951. 17.8% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the theory and applications of tensor decompositions, particularly in the context of multilinear algebra. It covers various decomposition methods such as Singular Value Decomposition, Parallel Factor Analysis, Canonical Polyadic Decomposition, and Tucker Decomposition, along with their applications in signal processing and machine learning.
- Tensor Decomposition
- Multilinear Algebra
- Singular Value Decomposition
- Parallel Factor Analysis
- Canonical Polyadic Decomposition
- Tucker Decomposition
- Nonnegative Tensor Factorization
- Higher-Order Tensors
- Signal Processing
- Machine Learning
- Research works
- 7.4k fractional, since 1951
- In the world top 10%
- 1.3k per year above
- Top-10% rate
- 17.8% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +53% the tick is no change
Which countries lead Tensor decomposition and applications research?
By volume, China and the United States publish the most (718 and 374 works in 2022–2025).
By volume, 2022–2025
- 1 China 718 works
- 2 United States 374 works
- 3 Germany 79 works
- 4 Japan 74 works
- 5 France 69 works
- 6 India 60 works
- 7 Italy 58 works
- 8 United Kingdom 54 works
- 9 Canada 42 works
- 10 Russia 34 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Tensor decomposition and applications research?
By volume in 2022–2025, University of Electronic Science and Technology of China publishes the most Tensor decomposition and applications research, followed by Southwest University and Fudan University.
By volume, 2022–2025
- 1 University of Electronic Science and Technology of ChinaChina 21 works
- 2 Southwest UniversityChina 18 works
- 3 Fudan UniversityChina 15 works
- 4 Shanghai UniversityChina 15 works
- 5 Xi'an Jiaotong UniversityChina 14 works
- 6 KU LeuvenBelgium 13 works
- 7 University of TrentoItaly 12 works
- 8 National University of Defense TechnologyChina 11 works
- 9 Tsinghua UniversityChina 11 works
- 10 Guangxi UniversityChina 10 works
Who are the leading researchers in Tensor decomposition and applications?
The most-cited researchers publishing on Tensor decomposition and applications include Dacheng Tao, Shuicheng Yan and Georgios B. Giannakis.
- 1 Dacheng Tao Australia 6.1k citations
- 2 Shuicheng Yan Singapore 4.6k citations
- 3 Georgios B. Giannakis United States 4.6k citations
- 4 Christos Faloutsos United States 4.5k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Tensor decomposition and applications research done?
The largest centres of Tensor decomposition and applications research in 2022–2025 are Beijing (China), Shanghai (China), Xi'an (China) and Guangzhou (China).
Where is the best place to study Tensor decomposition and applications?
Among universities, judged by research, University of Electronic Science and Technology of China, Southwest University and Fudan University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | University of Electronic Science and Technology of ChinaChina | 87.1 | 27.8% | 10.1× | 21 | +290.5% |
| 2 | Southwest UniversityChina | 77.2 | 30.8% | 17.9× | 18 | — |
| 3 | Fudan UniversityChina | 77.2 | 33.0% | 7.3× | 15 | +286.9% |
| 4 | Shanghai UniversityChina | 65.5 | 13.1% | 10.8× | 15 | +831.8% |
| 5 | Hunan UniversityChina | 60.2 | 23.6% | 7.6× | 10 | +418.3% |
| 6 | University of Hong KongHong Kong | 59.1 | 26.9% | 6.4× | 10 | — |
| 7 | Guangdong University of TechnologyChina | 53.3 | 19.1% | 8.1× | 8 | +283.3% |
| 8 | Xi'an Jiaotong UniversityChina | 49.7 | 21.2% | 4.7× | 14 | +93.0% |
| 9 | National University of Defense TechnologyChina | 48.5 | 15.8% | 7.6× | 11 | +127.0% |
| 10 | Tsinghua UniversityChina | 48.1 | 29.3% | 3.0× | 11 | +114.4% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Tensor decomposition and applications research growing?
Output in 2018–2022 was 53% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Tensor decomposition and applications.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.