Nico Vervliet

601 total citations
27 papers, 372 citations indexed

About

Nico Vervliet is a scholar working on Computational Mathematics, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Nico Vervliet has authored 27 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computational Mathematics, 9 papers in Computational Mechanics and 7 papers in Computational Theory and Mathematics. Recurrent topics in Nico Vervliet's work include Tensor decomposition and applications (23 papers), Sparse and Compressive Sensing Techniques (7 papers) and Matrix Theory and Algorithms (6 papers). Nico Vervliet is often cited by papers focused on Tensor decomposition and applications (23 papers), Sparse and Compressive Sensing Techniques (7 papers) and Matrix Theory and Algorithms (6 papers). Nico Vervliet collaborates with scholars based in Belgium, United States and China. Nico Vervliet's co-authors include Lieven De Lathauwer, Otto Debals, Laurent Sorber, Nele Moelans, Nicolas Gillis, Kejun Huang, Xiao Fu, Ignat Domanov, Sabine Van Huffel and Qiu‐Hua Lin and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Signal Processing Magazine and SIAM Journal on Scientific Computing.

In The Last Decade

Nico Vervliet

22 papers receiving 358 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Nico Vervliet Belgium	9	278	125	100	74	68	27	372
Otto Debals Belgium	9	268 1.0×	157 1.3×	185 1.9×	67 0.9×	60 0.9×	21	403
Dimitri Nion Belgium	11	328 1.2×	215 1.7×	467 4.7×	49 0.7×	72 1.1×	16	734
Rémy Boyer France	10	119 0.4×	57 0.5×	77 0.8×	25 0.3×	29 0.4×	22	415
Feng Yan China	10	41 0.1×	28 0.2×	22 0.2×	12 0.2×	88 1.3×	31	368
Ratikanta Behera India	9	34 0.1×	35 0.3×	59 0.6×	4 0.1×	61 0.9×	40	369
Luc Deneire France	15	55 0.2×	63 0.5×	105 1.1×	7 0.1×	59 0.9×	72	883
Tsui-Wei Weng United States	8	16 0.1×	11 0.1×	19 0.2×	5 0.1×	105 1.5×	20	271
Michael B. McCoy United States	4	7 0.0×	207 1.7×	71 0.7×	20 0.3×	40 0.6×	5	281
Galen Reeves United States	12	5 0.0×	261 2.1×	145 1.4×	15 0.2×	78 1.1×	36	446
Dennis Amelunxen Germany	6	6 0.0×	154 1.2×	31 0.3×	16 0.2×	35 0.5×	9	232

Countries citing papers authored by Nico Vervliet

Since Specialization

Citations

This map shows the geographic impact of Nico Vervliet'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 Nico Vervliet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nico Vervliet more than expected).

Fields of papers citing papers by Nico Vervliet

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nico Vervliet. 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 Nico Vervliet. The network helps show where Nico Vervliet may publish in the future.

Co-authorship network of co-authors of Nico Vervliet

This figure shows the co-authorship network connecting the top 25 collaborators of Nico Vervliet. A scholar is included among the top collaborators of Nico Vervliet 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 Nico Vervliet. Nico Vervliet is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Domanov, Ignat, et al.. (2024). Decomposition of a Tensor into Multilinear Rank-\({(M_{{r}},N_{{r}},\cdot )}\) Terms. SIAM Journal on Matrix Analysis and Applications. 45(3). 1310–1334. 1 indexed citations

Vervliet, Nico, et al.. (2024). A Bézoutian-Based Method for Solving Overdetermined Systems of Polynomial Equations. Lirias (KU Leuven). 1352–1356.

Bellemans, Inge, Nico Vervliet, Lieven De Lathauwer, Nele Moelans, & Kim Verbeken. (2022). Towards more realistic simulations of microstructural evolution in oxidic systems. Calphad. 77. 102402–102402. 2 indexed citations

Vervliet, Nico, et al.. (2022). Regression and Classification With Spline-Based Separable Expansions. Frontiers in Big Data. 5. 688496–688496. 1 indexed citations

Fu, Xiao, Nico Vervliet, Lieven De Lathauwer, Kejun Huang, & Nicolas Gillis. (2020). Nonconvex Optimization Tools for Large-Scale Matrix and Tensor Decomposition with Structured Factors.. Lirias (KU Leuven). 1 indexed citations

Vervliet, Nico, et al.. (2020). Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction. npj Computational Materials. 6(1). 19 indexed citations

Fu, Xiao, Nico Vervliet, Lieven De Lathauwer, Kejun Huang, & Nicolas Gillis. (2020). Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective. IEEE Signal Processing Magazine. 37(5). 78–94. 18 indexed citations

Vervliet, Nico, et al.. (2019). Algebraic and Optimization Based Algorithms for Multivariate Regression Using Symmetric Tensor Decomposition. Lirias (KU Leuven). 31. 475–479. 1 indexed citations

Vervliet, Nico, Otto Debals, & Lieven De Lathauwer. (2019). Exploiting Efficient Representations in Large-Scale Tensor Decompositions. SIAM Journal on Scientific Computing. 41(2). A789–A815. 19 indexed citations

10.

Vervliet, Nico, et al.. (2019). Identifying Stable Components of Matrix /Tensor Factorizations via Low-Rank Approximation of Inter-Factorization Similarity. Lirias (KU Leuven). 1–5. 5 indexed citations

11.

Vervliet, Nico, et al.. (2019). Rank-one Tensor Approximation with Beta-divergence Cost Functions. Lirias (KU Leuven). 1–5. 1 indexed citations

12.

Vervliet, Nico, et al.. (2018). CPD Updating Using Low-Rank Weights. Lirias (KU Leuven). 6368–6372.

13.

Vervliet, Nico, et al.. (2018). Linear systems with a canonical polyadic decomposition constrained solution: Algorithms and applications. Numerical Linear Algebra with Applications. 25(6). 17 indexed citations

14.

Vervliet, Nico, et al.. (2017). Face recognition as a kronecker product equation. Lirias (KU Leuven). 1–5. 6 indexed citations

15.

Vervliet, Nico, et al.. (2017). Nonlinear least squares updating of the canonical polyadic decomposition. Lirias (KU Leuven). 663–667. 13 indexed citations

16.

Gong, Xiao‐Feng, Qiu‐Hua Lin, Otto Debals, Nico Vervliet, & Lieven De Lathauwer. (2016). Coupled rank-(L_m, L_n,) block term decomposition by coupled block simultaneous generalized Schur decomposition. Lirias (KU Leuven). 2554–2558. 4 indexed citations

17.

Vervliet, Nico, Otto Debals, & Lieven De Lathauwer. (2016). Tensorlab 3.0 — Numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization. Lirias (KU Leuven). 1733–1738. 60 indexed citations

18.

Vervliet, Nico & Lieven De Lathauwer. (2015). A Randomized Block Sampling Approach to Canonical Polyadic Decomposition of Large-Scale Tensors. IEEE Journal of Selected Topics in Signal Processing. 10(2). 284–295. 52 indexed citations

19.

Vervliet, Nico, Otto Debals, Laurent Sorber, Marc Van Barel, & Lieven De Lathauwer. (2014). Structured data fusion using Tensorlab : a demonstration. 3 indexed citations

20.

Vervliet, Nico, Otto Debals, Laurent Sorber, & Lieven De Lathauwer. (2014). Breaking the Curse of Dimensionality using Decompositions of Incomplete Tensors. 4 indexed citations

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.

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