Nicolas Gillis

3.5k total citations · 1 hit paper
100 papers, 1.9k citations indexed

About

Nicolas Gillis is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Nicolas Gillis has authored 100 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Computational Mechanics, 39 papers in Computer Vision and Pattern Recognition and 31 papers in Media Technology. Recurrent topics in Nicolas Gillis's work include Sparse and Compressive Sensing Techniques (48 papers), Remote-Sensing Image Classification (29 papers) and Face and Expression Recognition (28 papers). Nicolas Gillis is often cited by papers focused on Sparse and Compressive Sensing Techniques (48 papers), Remote-Sensing Image Classification (29 papers) and Face and Expression Recognition (28 papers). Nicolas Gillis collaborates with scholars based in Belgium, United States and Canada. Nicolas Gillis's co-authors include François Glineur, Stephen A. Vavasis, Wing‐Kin Ma, Antonio Plaza, José M. Bioucas‐Dias, Tsung‐Han Chan, Paul Gader, ArulMurugan Ambikapathi, Chong‐Yung Chi and Robert Luce and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Automatica and European Journal of Operational Research.

In The Last Decade

Nicolas Gillis

89 papers receiving 1.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

2013 331 citations Nicolas Gillis et al. profile →

Peers

Nicolas Gillis

CVPR

MT

CM

SP

AI

Xiao Fu United States

Dao‐Qing Dai China

Fanhua Shang China

Kejun Huang United States

Byeungwoo Jeon South Korea

Jinshan Zeng China

Luoqing Li China

P. Tseng United States

Ning Hao China

Dacheng Tao United Kingdom

Xiao Fu United States

Nicolas Gillis

681 ×1.0

CVPR

461 ×0.8

MT

882 ×1.5

CM

713 ×1.9

SP

701 ×2.2

AI

Citations per year, relative to Nicolas Gillis Nicolas Gillis (= 1×) peers Xiao Fu

Countries citing papers authored by Nicolas Gillis

Since Specialization

Citations

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

Fields of papers citing papers by Nicolas Gillis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicolas Gillis

This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Gillis. A scholar is included among the top collaborators of Nicolas Gillis 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 Nicolas Gillis. Nicolas Gillis 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

1.

Gillis, Nicolas, et al.. (2025). Orthogonal nonnegative matrix factorization with the Kullback–Leibler divergence. Pattern Recognition Letters. 197. 353–358.

2.

Seyedi, Seyed Amjad, et al.. (2025). Distributionally robust nonnegative matrix factorization with self-paced adaptive multi-loss fusion. Information Sciences. 728. 122823–122823.

3.

Gillis, Nicolas, et al.. (2025). Block Majorization Minimization with Extrapolation and Application to \({\beta }\)-NMF. SIAM Journal on Mathematics of Data Science. 7(3). 1292–1314.

4.

Absil, Olivier, et al.. (2024). An alternating minimization algorithm with trajectory for direct exoplanet detection. Astronomy and Astrophysics. 692. A126–A126. 1 indexed citations

5.

Gillis, Nicolas, et al.. (2024). Orthogonal Symmetric Nonnegative Matrix Tri-Factorization. ORBi UMONS. 1–6. 1 indexed citations

6.

Gillis, Nicolas, et al.. (2024). Dual Simplex Volume Maximization for Simplex-Structured Matrix Factorization. SIAM Journal on Imaging Sciences. 17(4). 2362–2391. 1 indexed citations

7.

Gillis, Nicolas, et al.. (2021). Accelerating block coordinate descent for nonnegative tensor factorization. Numerical Linear Algebra with Applications. 28(5). 8 indexed citations

8.

Gillis, Nicolas, et al.. (2021). Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear Manifold Clustering Algorithms. arXiv (Cornell University). 24 indexed citations

9.

Gillis, Nicolas, et al.. (2020). A geometric lower bound on the extension complexity of polytopes based on the f -vector. Discrete Applied Mathematics. 303. 22–38. 1 indexed citations

10.

Gillis, Nicolas, et al.. (2020). Blind Audio Source Separation With Minimum-Volume Beta-Divergence NMF. IEEE Transactions on Signal Processing. 68. 3400–3410. 49 indexed citations

11.

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

12.

Gillis, Nicolas, et al.. (2019). Algorithms and Comparisons of Nonnegative Matrix Factorizations With Volume Regularization for Hyperspectral Unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(12). 4843–4853. 33 indexed citations

13.

Ahookhosh, Masoud, et al.. (2019). . arXiv (Cornell University). 18 indexed citations

14.

Gillis, Nicolas, et al.. (2019). Inertial Block Mirror Descent Method for Non-Convex Non-Smooth Optimization. Lirias (KU Leuven). 3 indexed citations

15.

Cohen, Jérémy E. & Nicolas Gillis. (2018). Identifiability of Low-Rank Sparse Component Analysis.. arXiv (Cornell University). 1 indexed citations

16.

Gillis, Nicolas & Robert Luce. (2017). A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary. IEEE Transactions on Image Processing. 27(1). 24–37. 19 indexed citations

17.

Gillis, Nicolas, et al.. (2017). On the linear extension complexity of regular n-gons. Linear Algebra and its Applications. 521. 217–239. 3 indexed citations

18.

Gillis, Nicolas, et al.. (2013). ONP-MF: An Orthogonal Nonnegative Matrix Factorization Algorithm with Application to Clustering. ORBi UMONS. 4 indexed citations

19.

Gillis, Nicolas & François Glineur. (2012). On the geometric interpretation of the nonnegative rank. Linear Algebra and its Applications. 437(11). 2685–2712. 41 indexed citations

20.

Gillis, Nicolas & Robert J. Plemmons. (2012). Sparse nonnegative matrix underapproximation and its application to hyperspectral image analysis. Linear Algebra and its Applications. 438(10). 3991–4007. 33 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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