Marco Signoretto
- Computational Mechanics top 5%
- Computational Mathematics top 0.5%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 5%
- Cognitive Neuroscience
- Co-authors
- Johan A. K. SuykensLieven De LathauwerQuoc Tran DinhBart De MoorRaf Van de PlasMaarten De VosToon van WaterschootMarc Moonen
- Topics
- Sparse and Compressive Sensing Techniques (6 papers)Blind Source Separation Techniques (4 papers)EEG and Brain-Computer Interfaces (4 papers)
- Partner nations
- BelgiumSwitzerlandGermany
In The Last Decade
Marco Signoretto
16 papers receiving 459 citations
Peers
Comparison fields: 5 of 66
- Computational Mechanics 222
- Computational Mathematics 218
- Computer Vision and Pattern Recognition 127
- Signal Processing 117
- Cognitive Neuroscience 67
Countries citing papers authored by Marco Signoretto
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Hybrid algorithms with applications to sparse and low rank regularization | 0 |
| 3 | 6 | |
| 4 | An SVD-free Approach to a Class of Structured Low Rank Matrix Optimization Problems with Application to System Identification | 23 |
| 5 | International workshop on advances in regularization, optimization, kernel methods and support vector machines : theory and applications (ROKS 2013) | 1 |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 48 | |
| 9 | 142 | |
| 10 | Joint Regression and Linear Combination of Time Series for Optimal Prediction | 0 |
| 11 | Correcting electrode displacement errors in motor unit tracking using high density surface electromyography (HDsEMG) | 1 |
| 12 | 57 | |
| 13 | 6 | |
| 14 | 9 | |
| 15 | 68 | |
| 16 | 101 | |
| 17 | 8 | |
| 18 | Data-Dependent Norm Adaptation for Sparse Recovery in Kernel Ensembles Learning | 1 |
About Marco Signoretto
Marco Signoretto is a scholar working on Computational Mathematics, Signal Processing and Computational Mechanics, having authored 18 papers that have together received 476 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (6 papers), Blind Source Separation Techniques (4 papers) and EEG and Brain-Computer Interfaces (4 papers). The work is most often cited by research in Computational Mathematics (218 citations), Signal Processing (117 citations) and Computational Mechanics (222 citations). Marco Signoretto has collaborated with scholars based in Belgium, Switzerland and Germany. Frequent co-authors include Johan A. K. Suykens, Lieven De Lathauwer, Quoc Tran Dinh, Bart De Moor, Raf Van de Plas, Maarten De Vos, Toon van Waterschoot, Marc Moonen, Wim Van Paesschen and Sabine Van Huffel. Their work appears in journals such as PLoS ONE, IEEE Transactions on Signal Processing and Neural Networks.
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.