Results 51 to 60 of about 13,511,913 (89)

Algorithms and literate programs for weighted low-rank approximation with missing data

, 2011
Linear models identification from data with missing values is posed as a weighted low-rank approximation problem with weights related to the missing values equal to zero.
Markovsky, Ivan
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Optimal one-sided approximants of circular arc [PDF]

The optimal one-sided parametric polynomial approximants of a circular arc are considered. More precisely, the approximant must be entirely in or out of the underlying circle of an arc.
Vavpetič, Aleš, Praprotnik, Ada Šadl, Žagar, Emil   +2 more
core   +1 more source

Randomized methods for dynamical low-rank approximation

We introduce novel dynamical low-rank methods for solving large-scale matrix differential equa- tions, motivated by algorithms from randomized numerical linear algebra.
Carrel, Benjamin
core   +1 more source

Constrained Low-Rank Learning Using Least Squares-Based Regularization

, 2017
Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization.
Jun Yu, Deng Cai, Ping Li, Li, Ping, Luming Zhang, Zhang, Luming, Cai, Deng, Li, Xuelong, Meng Wang, Wang, Meng, Xuelong Li, Yu, Jun   +11 more
core   +1 more source

Rank reduction of correlation matrices by majorization [PDF]

In this paper a novel method is developed for the problem of finding a low-rank correlation matrix nearest to a given correlation matrix. The method is based on majorization and therefore it is globally convergent. The method is computationally efficient,
Groenen, P.J.F., Pietersz, R.
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Symplectic dynamical low rank approximation of wave equations with random parameters [PDF]

In this paper we propose a dynamical low-rank strategy for the approximation of second order wave equations with random parameters. The governing equation is rewritten in Hamiltonian form and the approximate solution is expanded over a set of 2S ...
Nobile, Fabio, Vidlicková, Eva, Musharbash, Eleonora   +2 more
core   +1 more source

Learning low-rank kernel matrices

, 2006
Kernel learning plays an important role in many machine learning tasks. However, algorithms for learning a kernel matrix often scale poorly, with running times that are cubic in the number of data points.
Mátyás Sustik, Brian Kulis, Inderjit Dhillon   +2 more
core   +1 more source

Low-Complexity Reduced-Rank Adaptive Detection in Hybrid Direct-Sequence Time-Hopping Ultrawide Bandwidth Systems

, 2009
— In this contribution reduced-rank adaptive minimum mean-square error multiuser detector (MMSE-MUD) is proposed and investi-gated for the hybrid direct-sequence time-hopping ultrawide bandwidth (DS-TH UWB) systems.
Ahmed, Qasim, Lie-liang Yang, Yang, Lie-Liang, Qasim Zeeshan Ahmed   +3 more
core   +1 more source

Low-Rank Online Metric Learning

, 2014
Image classification is a key problem in computer vision community. Most of the conventional visual recognition systems usually train an image classifier in an offline batch mode with all training data provided in advance. Unfortunately in many practical
Liu J(刘霁), Yuan JS(袁浚菘), Luo JB(罗杰波), Cong Y(丛杨)   +3 more
core  

Error estimates for SUPG-stabilised Dynamical Low Rank Approximations [PDF]

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems.
Nobile, Fabio, Trigo Trindade, Thomas Simon Spencer   +1 more
core   +1 more source