What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Pattern recognition, Cluster analysis, Computer vision and Linear subspace.
His Artificial intelligence research incorporates elements of Algorithm and Affine transformation.
His studies in Pattern recognition integrate themes in fields like Singular value, Spectral clustering and Singular value decomposition.
His Cluster analysis research includes elements of Identification, Theoretical computer science and Dimensionality reduction.
His biological study spans a wide range of topics, including Linear dynamical system and Benchmark.
René Vidal interconnects Data point and Sparse matrix in the investigation of issues within Linear subspace.
His most cited work include:
- Sparse Subspace Clustering: Algorithm, Theory, and Applications (1716 citations)
- Sparse subspace clustering (1103 citations)
- Generalized principal component analysis (GPCA) (823 citations)
What are the main themes of his work throughout his whole career to date?
René Vidal focuses on Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Cluster analysis.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning.
The Computer vision study combines topics in areas such as Linear dynamical system and Affine transformation.
His study in Algorithm is interdisciplinary in nature, drawing from both Dimension, Matrix and Mathematical optimization.
The Pattern recognition study combines topics in areas such as 3D pose estimation and Pose.
René Vidal works mostly in the field of Cluster analysis, limiting it down to topics relating to Linear subspace and, in certain cases, Subspace topology, Data point, Synthetic data and Polynomial.
He most often published in these fields:
- Artificial intelligence (53.02%)
- Computer vision (26.25%)
- Algorithm (23.62%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (53.02%)
- Algorithm (23.62%)
- Applied mathematics (9.97%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Algorithm, Applied mathematics, Linear subspace and Cluster analysis.
His Artificial intelligence research includes themes of Machine learning, Computer vision and Pattern recognition.
In general Algorithm study, his work on Regularization often relates to the realm of Diffusion MRI, thereby connecting several areas of interest.
His Applied mathematics research incorporates elements of Dynamical systems theory, Matrix decomposition, Gradient descent, Differential equation and Discretization.
His Linear subspace research incorporates themes from Data point and Subspace topology.
He usually deals with Cluster analysis and limits it to topics linked to Dimension and Embedding.
Between 2018 and 2021, his most popular works were:
- Nonconvex Robust Low-Rank Matrix Recovery (21 citations)
- Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications (19 citations)
- Conformal Symplectic and Relativistic Optimization (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
René Vidal mainly investigates Applied mathematics, Optimization problem, Differential equation, Algorithm and Dynamical systems theory.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Discretization, Matrix, Subgradient method and Outlier.
His study focuses on the intersection of Subgradient method and fields such as Grassmannian with connections in the field of Subspace topology.
His Optimization problem study combines topics from a wide range of disciplines, such as Imaging science, Regularization, Blind deconvolution and Signal processing.
The study incorporates disciplines such as Matrix norm, Linear combination, Regular polygon, Matrix decomposition and Principal component analysis in addition to Algorithm.
His Dynamical systems theory study incorporates themes from Symplectic geometry and Hamiltonian system.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
