Concept
Low-rank approximation
Related publications (27)
Randomized low-rank approximation and its applications
In this thesis we will present and analyze randomized algorithms for numerical linear algebra problems. An important theme in this thesis is randomized low-rank approximation. In particular, we will study randomized low-rank approximation of matrix functio ...
EPFL2024Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World
In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches have shown remarkable success on synthetic datasets, we have observed them to fail in the presence of real-world data. We ...
Ieee Computer Soc2024Dynamical low rank approximation for uncertainty quantification of time-dependent problems
The quantification of uncertainties can be particularly challenging for problems requiring long-time integration as the structure of the random solution might considerably change over time. In this respect, dynamical low-rank approximation (DLRA) is very a ...
EPFL2022Stability properties of a projector-splitting scheme for dynamical low rank approximation of random parabolic equations
Fabio Nobile, Yoshihito Kazashi, Eva Vidlicková
We consider the Dynamical Low Rank (DLR) approximation of random parabolic equations and propose a class of fully discrete numerical schemes. Similarly to the continuous DLR approximation, our schemes are shown to satisfy a discrete variational formulation ...
SPRINGER HEIDELBERG2021MATHICSE Technical Report: Stability properties of a projector-splitting scheme for the dynamical low rank approximation of random parabolic equations
Fabio Nobile, Yoshihito Kazashi, Eva Vidlicková
We consider the Dynamical Low Rank (DLR) approximation of random parabolic equations and propose a class of fully discrete numerical schemes. Similarly to the continuous DLR approximation, our schemes are shown to satisfy a discrete variational formulation ...
MATHICSE2020Streaming Low-Rank Matrix Approximation With An Application To Scientific Simulation
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collection. The technical contribution consists in a new algorithm for constructing ...
2019Tensor Robust Pca On Graphs
Pierre Vandergheynst, Nauman Shahid, Francesco Grassi
We propose a graph signal processing framework to overcome the computational burden of Tensor Robust PCA (TRPCA). Our framework also serves as a convex alternative to graph regularized tensor factorization methods. Our method is based on projecting a tenso ...
IEEE2019Monopulse Beam Synthesis Using a Sparse Single Layer of Weights
A conventional monopulse radar system uses three beams, namely, sum beam, elevation difference beam, and azimuth difference beam, which require different layers of weights to synthesize each beam independently. Since the multilayer structure increases the ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019Solving Rank-Structured Sylvester And Lyapunov Equations
We consider the problem of efficiently solving Sylvester and Lyapunov equations of medium and large scale, in case of rank-structured data, i.e., when the coefficient matrices and the right-hand side have low-rank off-diagonal blocks. This comprises proble ...
SIAM PUBLICATIONS2018MRI artifact correction using sparse plus low-rank decomposition of annihilating filter-based hankel matrix
PurposeMagnetic resonance imaging (MRI) artifacts are originated from various sources including instability of an magnetic resonance (MR) system, patient motion, inhomogeneities of gradient fields, and so on. Such MRI artifacts are usually considered as ir ...
Wiley2017