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- Lieven De Lathauwer

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
48
Citations
16,686
272
World Ranking
864
National Ranking
12

Engineering and Technology
D-index
48
Citations
16,698
260
World Ranking
2204
National Ranking
29

2017 - SIAM Fellow For fundamental contributions to theory, computation, and application of tensor decompositions.

2015 - IEEE Fellow For contributions to signal processing algorithms using tensor decompositions

- 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.

- 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)

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.

- Tensor (26.52%)
- Algebra (21.73%)
- Algorithm (21.73%)

- Tensor (26.52%)
- Algorithm (21.73%)
- Matrix decomposition (13.74%)

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.

- 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)

- 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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

A Multilinear Singular Value Decomposition

Lieven De Lathauwer;Bart De Moor;Joos Vandewalle.

SIAM Journal on Matrix Analysis and Applications **(2000)**

4510 Citations

On the Best Rank-1 and Rank-( R 1 , R 2 ,. . ., R N ) Approximation of Higher-Order Tensors

Lieven De Lathauwer;Bart De Moor;Joos Vandewalle.

SIAM Journal on Matrix Analysis and Applications **(2000)**

1811 Citations

Tensor Decomposition for Signal Processing and Machine Learning

Nicholas D. Sidiropoulos;Lieven De Lathauwer;Xiao Fu;Kejun Huang.

IEEE Transactions on Signal Processing **(2017)**

1238 Citations

Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis

Andrzej Cichocki;Danilo Mandic;Lieven De Lathauwer;Guoxu Zhou.

IEEE Signal Processing Magazine **(2015)**

1212 Citations

Signal Processing based on Multilinear Algebra

Lieven De Lathauwer.

**(1997)**

519 Citations

A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization

Lieven De Lathauwer.

SIAM Journal on Matrix Analysis and Applications **(2006)**

461 Citations

Decompositions of a Higher-Order Tensor in Block Terms—Part II: Definitions and Uniqueness

Lieven De Lathauwer.

SIAM Journal on Matrix Analysis and Applications **(2008)**

430 Citations

Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-$(L_r,L_r,1)$ Terms, and a New Generalization

Laurent Sorber;Marc Van Barel;Lieven De Lathauwer.

Siam Journal on Optimization **(2013)**

310 Citations

Computation of the Canonical Decomposition by Means of a Simultaneous Generalized Schur Decomposition

Lieven De Lathauwer;Bart De Moor;Joos Vandewalle.

SIAM Journal on Matrix Analysis and Applications **(2005)**

249 Citations

An introduction to independent component analysis

Lieven De Lathauwer;Bart De Moor;Joos Vandewalle.

Journal of Chemometrics **(2000)**

243 Citations

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