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.
Sparse Subspace Clustering: Algorithm, Theory, and Applications
This map shows the geographic impact of Renè Vidal'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 Renè Vidal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Renè Vidal more than expected).
This network shows the impact of papers produced by Renè Vidal. 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 Renè Vidal. The network helps show where Renè Vidal may publish in the future.
Co-authorship network of co-authors of Renè Vidal
This figure shows the co-authorship network connecting the top 25 collaborators of Renè Vidal.
A scholar is included among the top collaborators of Renè Vidal 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 Renè Vidal. Renè Vidal is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhu, Zhihui, et al.. (2019). A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning. Neural Information Processing Systems. 32. 9437–9447.6 indexed citations
6.
Ma, Yi, et al.. (2018). New Rank Deficiency Condition for Multiple View Geometry of Point Features. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign).
7.
You, Chong, Chi Li, Daniel P. Robinson, & Renè Vidal. (2018). Scalable Exemplar-based Subspace Clustering on Class-Imbalanced Data. 67–83.15 indexed citations
Haeffele, Benjamin D., et al.. (2018). Separable Dictionary Learning with Global Optimality and Applications to Diffusion MRI.. arXiv (Cornell University).2 indexed citations
Cavazza, Jacopo, Pietro Morerio, Benjamin D. Haeffele, et al.. (2017). Dropout as a Low-Rank Regularizer for Matrix Factorization.. International Conference on Artificial Intelligence and Statistics. 435–444.3 indexed citations
12.
Ali, Haider, et al.. (2017). Joint Object Category and 3D Pose Estimation from 2D Images. arXiv (Cornell University).1 indexed citations
Robinson, Daniel P., et al.. (2015). Sparse Subspace Clustering with Missing Entries. International Conference on Machine Learning. 2463–2472.41 indexed citations
Paoletti, Simone, A.L. Juloski, Giancarlo Ferrari‐Trecate, & Renè Vidal. (2007). Identification of hybrid systems: a tutorial. Infoscience (Ecole Polytechnique Fédérale de Lausanne).
20.
Vidal, Renè, et al.. (2006). Nonrigid Shape and Motion from Multiple Perspective Views. Lecture notes in computer science. 3952. 205–218.5 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.