What is he best known for?
The fields of study he is best known for:
- Algebra
- Statistics
- Mathematical analysis
Lieven De Lathauwer mostly deals with Multilinear algebra, Tensor, Rank, Multilinear map and Tensor.
His Tensor research incorporates elements of Matrix, Theoretical computer science and Data analysis.
He is interested in Multilinear subspace learning, which is a branch of Multilinear map.
His Multilinear subspace learning research integrates issues from Multilinear principal component analysis, Signal processing and Singular value decomposition, Higher-order singular value decomposition.
The study incorporates disciplines such as Independent component analysis and LU decomposition in addition to Singular value decomposition.
His Tensor research is multidisciplinary, incorporating perspectives in Matrix decomposition, Algorithm, Eigendecomposition of a matrix and Schur decomposition.
His most cited work include:
- A Multilinear Singular Value Decomposition (3096 citations)
- On the Best Rank-1 and Rank-( R 1 , R 2 ,. . ., R N ) Approximation of Higher-Order Tensors (1219 citations)
- Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (742 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Tensor, Algebra, Algorithm, Tensor and Multilinear algebra.
He combines subjects such as Matrix, Uniqueness, Algebraic number and Rank with his study of Tensor.
His Rank research includes elements of Discrete mathematics, Multilinear map, Pure mathematics, Combinatorics and Linear algebra.
His work in Multilinear subspace learning and Multilinear principal component analysis is related to Multilinear map.
His research in Algorithm intersects with topics in Matrix decomposition, Mathematical optimization, Blind signal separation and Signal processing.
His work in Tensor tackles topics such as Artificial intelligence which are related to areas like Machine learning and Electroencephalography.
He most often published in these fields:
- Tensor (26.52%)
- Algebra (21.73%)
- Algorithm (21.73%)
What were the highlights of his more recent work (between 2015-2021)?
- Tensor (26.52%)
- Algorithm (21.73%)
- Matrix decomposition (13.74%)
In recent papers he was focusing on the following fields of study:
Lieven De Lathauwer mostly deals with Tensor, Algorithm, Matrix decomposition, Tensor and Matrix.
His research integrates issues of Signal-to-noise ratio, Variety and Applied mathematics in his study of Tensor.
His Matrix decomposition study integrates concerns from other disciplines, such as Theoretical computer science, Blind signal separation, Diagonalizable matrix, Identifiability and Range.
His biological study spans a wide range of topics, including Factorization, Multilinear map and Pattern recognition.
His Multilinear map research is multidisciplinary, relying on both Artificial intelligence, Combinatorics and Rank.
Lieven De Lathauwer merges many fields, such as Pure mathematics and Multilinear algebra, in his writings.
Between 2015 and 2021, his most popular works were:
- Tensor Decomposition for Signal Processing and Machine Learning (639 citations)
- Tensorlab 3.0 — Numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization (48 citations)
- A Tensor-Based Method for Large-Scale Blind Source Separation Using Segmentation (47 citations)
In his most recent research, the most cited papers focused on:
- Algebra
- Statistics
- Mathematical analysis
His primary scientific interests are in Matrix decomposition, Algorithm, Tensor, Uniqueness and Decomposition.
The concepts of his Matrix decomposition study are interwoven with issues in Diagonalizable matrix, Identifiability and Harmonic.
His work deals with themes such as Mathematical optimization and Blind signal separation, which intersect with Algorithm.
His Tensor study combines topics from a wide range of disciplines, such as Theoretical computer science, Singular value decomposition, Variety, Algebra and Signal processing.
He focuses mostly in the field of Tensor, narrowing it down to matters related to Multilinear map and, in some cases, Feature vector.
His study in Rank is interdisciplinary in nature, drawing from both Topic model, Machine learning, Multilinear subspace learning and Data analysis.
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