Topic · Computational Mathematics
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. 10.2% 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%
- 756 per year above
- Top-10% rate
- 10.2% share of its works in the world top 10%
- Growth, 2018-21 → 2022-25
- +24% the tick is no change
- Largest producer
China
48% of the 10 largest, 2022-now
Drawing this view