Kim Batselier

745 total citations
51 papers, 457 citations indexed

About

Kim Batselier is a scholar working on Computational Mathematics, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Kim Batselier has authored 51 papers receiving a total of 457 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computational Mathematics, 19 papers in Computational Mechanics and 18 papers in Computational Theory and Mathematics. Recurrent topics in Kim Batselier's work include Tensor decomposition and applications (31 papers), Model Reduction and Neural Networks (16 papers) and Advanced Adaptive Filtering Techniques (13 papers). Kim Batselier is often cited by papers focused on Tensor decomposition and applications (31 papers), Model Reduction and Neural Networks (16 papers) and Advanced Adaptive Filtering Techniques (13 papers). Kim Batselier collaborates with scholars based in Hong Kong, Netherlands and Belgium. Kim Batselier's co-authors include Ngai Wong, Philippe Dreesen, Bart De Moor, Wenjian Yu, Luca Daniel, Jianchun Li, Bart De Moor, Cong Chen, Johan A. K. Suykens and Zheng Zhang and has published in prestigious journals such as Automatica, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Kim Batselier

48 papers receiving 444 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kim Batselier Hong Kong 14 212 153 96 87 77 51 457
Mariya Ishteva Belgium 9 175 0.8× 98 0.6× 83 0.9× 52 0.6× 33 0.4× 26 301
Otto Debals Belgium 9 268 1.3× 157 1.0× 34 0.4× 60 0.7× 25 0.3× 21 403
Berkant Savas Sweden 10 282 1.3× 151 1.0× 133 1.4× 140 1.6× 127 1.6× 19 531
Chunfeng Cui China 12 158 0.7× 74 0.5× 143 1.5× 41 0.5× 53 0.7× 39 394
Bamdev Mishra Japan 10 91 0.4× 221 1.4× 53 0.6× 136 1.6× 17 0.2× 31 403
Zhening Li United Kingdom 12 255 1.2× 127 0.8× 213 2.2× 42 0.5× 8 0.1× 28 444
Qibin Zhao Japan 6 481 2.3× 229 1.5× 55 0.6× 137 1.6× 92 1.2× 9 672
Guang-Xin Huang China 10 43 0.2× 73 0.5× 239 2.5× 61 0.7× 13 0.2× 35 413
Feng Yan China 10 41 0.2× 28 0.2× 51 0.5× 88 1.0× 17 0.2× 31 368
Dimitri Nion Belgium 11 328 1.5× 215 1.4× 27 0.3× 72 0.8× 8 0.1× 16 734

Countries citing papers authored by Kim Batselier

Since Specialization
Citations

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

Fields of papers citing papers by Kim Batselier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kim Batselier

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

All Works

20 of 20 papers shown
1.
Batselier, Kim. (2023). A Khatri-Rao product tensor network for efficient symmetric MIMO Volterra identification. IFAC-PapersOnLine. 56(2). 7282–7287. 1 indexed citations
2.
Batselier, Kim, et al.. (2023). Bayesian Framework for a MIMO Volterra Tensor Network. IFAC-PapersOnLine. 56(2). 7294–7299. 1 indexed citations
3.
Yu, Wenjian, et al.. (2021). Faster tensor train decomposition for sparse data. Journal of Computational and Applied Mathematics. 405. 113972–113972. 12 indexed citations
4.
Batselier, Kim, et al.. (2019). Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identification. Research Repository (Delft University of Technology). 7148–7153. 2 indexed citations
5.
Dreesen, Philippe, Kim Batselier, & Bart De Moor. (2017). Multidimensional realisation theory and polynomial system solving. International Journal of Control. 91(12). 2692–2704. 13 indexed citations
6.
Chen, Cong, et al.. (2017). Tensor-network-based predistorter design for multiple-input multiple-output nonlinear systems. 1117–1120. 2 indexed citations
7.
Batselier, Kim, et al.. (2017). An efficient homotopy-based Poincaré-Lindstedt method for the periodic steady-state analysis of nonlinear autonomous oscillators. The HKU Scholars Hub (University of Hong Kong). 283–288. 3 indexed citations
8.
Batselier, Kim, et al.. (2017). A Tensor Network Kalman filter with an application in recursive MIMO Volterra system identification. Automatica. 84. 17–25. 27 indexed citations
9.
Batselier, Kim, et al.. (2016). Parallelized Tensor Train Learning For Polynomial Pattern Classification. arXiv (Cornell University). 1 indexed citations
10.
Batselier, Kim & Ngai Wong. (2016). Symmetric tensor decomposition by an iterative eigendecomposition algorithm. Journal of Computational and Applied Mathematics. 308. 69–82. 9 indexed citations
11.
Li, Jianchun, Xiaoyan Xiong, Kim Batselier, et al.. (2015). STAVES: Speedy Tensor-Aided Volterra-Based Electronic Simulator. International Conference on Computer Aided Design. 583–588. 3 indexed citations
12.
Li, Jianchun, Kim Batselier, & Ngai Wong. (2014). A novel linear algebra method for the determination of periodic steady states of nonlinear oscillators. International Conference on Computer Aided Design. 611–617. 2 indexed citations
13.
Batselier, Kim, Philippe Dreesen, & Bart De Moor. (2014). On the null spaces of the Macaulay matrix. Linear Algebra and its Applications. 460. 259–289. 11 indexed citations
14.
Batselier, Kim, Philippe Dreesen, & Bart De Moor. (2014). A fast iterative orthogonalization scheme for the Macaulay matrix. SIAM Journal on Scientific Computing. 267. 20–32. 2 indexed citations
15.
Batselier, Kim, Philippe Dreesen, & Bart De Moor. (2014). A fast recursive orthogonalization scheme for the Macaulay matrix. Journal of Computational and Applied Mathematics. 267. 20–32. 9 indexed citations
16.
Li, Jianchun, Kim Batselier, & Ngai Wong. (2014). A novel linear algebra method for the determination of periodic steady states of nonlinear oscillators. The HKU Scholars Hub (University of Hong Kong). 32. 611–617. 3 indexed citations
17.
Batselier, Kim, Philippe Dreesen, & Bart De Moor. (2013). A geometrical approach to finding multivariate approximate LCMs and GCDs. Linear Algebra and its Applications. 438(9). 3618–3628. 5 indexed citations
18.
Dreesen, Philippe, Kim Batselier, & Bart De Moor. (2012). Weighted/structured total least squares problems and polynomial system solving. The European Symposium on Artificial Neural Networks. 351–356.
19.
Batselier, Kim, Philippe Dreesen, Marco Signoretto, et al.. (2012). Joint Regression and Linear Combination of Time Series for Optimal Prediction. The European Symposium on Artificial Neural Networks.
20.
Batselier, Kim, Philippe Dreesen, & Bart De Moor. (2012). Prediction Error Method Identification is an Eigenvalue Problem. IFAC Proceedings Volumes. 45(16). 221–226. 8 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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