Marco Signoretto

737 total citations
18 papers, 476 citations indexed

About

Marco Signoretto is a scholar working on Computational Mechanics, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Marco Signoretto has authored 18 papers receiving a total of 476 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Mechanics, 5 papers in Artificial Intelligence and 4 papers in Cognitive Neuroscience. Recurrent topics in Marco Signoretto's work include Sparse and Compressive Sensing Techniques (6 papers), Blind Source Separation Techniques (4 papers) and EEG and Brain-Computer Interfaces (4 papers). Marco Signoretto is often cited by papers focused on Sparse and Compressive Sensing Techniques (6 papers), Blind Source Separation Techniques (4 papers) and EEG and Brain-Computer Interfaces (4 papers). Marco Signoretto collaborates with scholars based in Belgium, Switzerland and Germany. Marco Signoretto's co-authors include Johan A. K. Suykens, Lieven De Lathauwer, Quoc Tran Dinh, Raf Van de Plas, Bart De Moor, Maarten De Vos, Toon van Waterschoot, Marc Moonen, Søren Holdt Jensen and Borbála Hunyadi and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Signal Processing and Neural Networks.

In The Last Decade

Marco Signoretto

16 papers receiving 459 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Signoretto Belgium 8 222 218 127 117 67 18 476
Xiao‐Feng Gong China 15 116 0.5× 206 0.9× 37 0.3× 410 3.5× 156 2.3× 61 741
Shaoguang Huang China 13 138 0.6× 56 0.3× 303 2.4× 50 0.4× 13 0.2× 44 551
Syed Zubair Pakistan 12 185 0.8× 35 0.2× 87 0.7× 111 0.9× 24 0.4× 21 480
Dimitri Nion Belgium 11 215 1.0× 328 1.5× 25 0.2× 467 4.0× 10 0.1× 16 734
Bijan Afsari United States 10 60 0.3× 27 0.1× 87 0.7× 75 0.6× 28 0.4× 20 443
Zbyněk Koldovský Czechia 17 330 1.5× 68 0.3× 82 0.6× 943 8.1× 190 2.8× 78 1.1k
Vicente Zarzoso France 15 179 0.8× 64 0.3× 54 0.4× 843 7.2× 352 5.3× 98 1.3k
Jize Xue China 15 476 2.1× 222 1.0× 785 6.2× 41 0.4× 5 0.1× 27 1.1k
Zhaoshui He China 4 77 0.3× 36 0.2× 102 0.8× 152 1.3× 15 0.2× 6 334
Junmei Yang China 12 127 0.6× 26 0.1× 110 0.9× 200 1.7× 12 0.2× 35 601

Countries citing papers authored by Marco Signoretto

Since Specialization
Citations

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

Fields of papers citing papers by Marco Signoretto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Signoretto

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

All Works

18 of 18 papers shown
1.
Sleutjes, Boudewijn T.H.M., Maarten De Vos, Joleen H. Blok, et al.. (2014). Motor Unit Tracking Using High Density Surface Electromyography (HDsEMG). Methods of Information in Medicine. 54(3). 221–226. 2 indexed citations
2.
Argyriou, Andreas A., Marco Signoretto, & Johan A. K. Suykens. (2014). Hybrid algorithms with applications to sparse and low rank regularization.
3.
Signoretto, Marco, et al.. (2014). High level high performance computing for multitask learning of time-varying models. Lirias (KU Leuven). 30. 1–6. 6 indexed citations
4.
Suykens, Johan A. K., Andreas A. Argyriou, Kris De Brabanter, et al.. (2013). International workshop on advances in regularization, optimization, kernel methods and support vector machines : theory and applications (ROKS 2013). Lirias (KU Leuven). 1–128. 1 indexed citations
5.
Signoretto, Marco, Volkan Cevher, & Johan A. K. Suykens. (2013). An SVD-free Approach to a Class of Structured Low Rank Matrix Optimization Problems with Application to System Identification. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 23 indexed citations
6.
Hunyadi, Borbála, Marco Signoretto, Stefan Debener, Sabine Van Huffel, & Maarten De Vos. (2013). Classification of Structured EEG Tensors Using Nuclear Norm Regularization: Improving P300 Classification. Lirias (KU Leuven). 110. 98–101. 2 indexed citations
7.
Signoretto, Marco & Johan A. K. Suykens. (2013). DynOpt: Incorporating dynamics into mean-variance portfolio optimization. Lirias (KU Leuven). 48–54. 1 indexed citations
8.
Signoretto, Marco, et al.. (2013). Nonlinear Acoustic Echo Cancellation Based on a Sliding-Window Leaky Kernel Affine Projection Algorithm. IEEE Transactions on Audio Speech and Language Processing. 21(9). 1867–1878. 48 indexed citations
9.
Signoretto, Marco, Quoc Tran Dinh, Lieven De Lathauwer, & Johan A. K. Suykens. (2013). Learning with tensors: a framework based on convex optimization and spectral regularization. Machine Learning. 94(3). 303–351. 142 indexed citations
10.
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.
11.
Gligorijević, Ivan, Boudewijn T.H.M. Sleutjes, Maarten De Vos, et al.. (2012). Correcting electrode displacement errors in motor unit tracking using high density surface electromyography (HDsEMG). Methods of Information in Medicine. 1–4. 1 indexed citations
12.
Signoretto, Marco & Johan A. K. Suykens. (2012). Convex Estimation of Cointegrated VAR Models by a Nuclear Norm Penalty. IFAC Proceedings Volumes. 45(16). 95–100. 6 indexed citations
13.
Hunyadi, Borbála, Marco Signoretto, Wim Van Paesschen, et al.. (2012). Incorporating structural information from the multichannel EEG improves patient-specific seizure detection. Clinical Neurophysiology. 123(12). 2352–2361. 57 indexed citations
14.
Signoretto, Marco, Emanuele Olivetti, Lieven De Lathauwer, & Johan A. K. Suykens. (2012). Classification of Multichannel Signals With Cumulant-Based Kernels. IEEE Transactions on Signal Processing. 60(5). 2304–2314. 9 indexed citations
15.
Signoretto, Marco, Lieven De Lathauwer, & Johan A. K. Suykens. (2011). A kernel-based framework to tensorial data analysis. Neural Networks. 24(8). 861–874. 68 indexed citations
16.
Signoretto, Marco, Raf Van de Plas, Bart De Moor, & Johan A. K. Suykens. (2011). Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data. IEEE Signal Processing Letters. 18(7). 403–406. 101 indexed citations
17.
Daemen, Anneleen, Marco Signoretto, Olivier Gevaert, Johan A. K. Suykens, & Bart De Moor. (2010). Improved Microarray-Based Decision Support with Graph Encoded Interactome Data. PLoS ONE. 5(4). e10225–e10225. 8 indexed citations
18.
Signoretto, Marco, Kristiaan Pelckmans, Lieven De Lathauwer, & Johan A. K. Suykens. (2009). Data-Dependent Norm Adaptation for Sparse Recovery in Kernel Ensembles Learning. 1 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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