Results 61 to 70 of about 13,511,913 (89)
A convergent reformulation of perturbative QCD [PDF]
, 2000 We present and explore a new formulation of perturbative QCD based not on the renormalised coupling but on the dimensional transmutation parameter of the theory and the property of asymptotic scaling.
Alves(^1), Ricardo João Gaio
core
Learning low-rank output kernels
, 2011 Output kernel learning techniques allow to simultaneously learn a vector-valued function and a positive semidefinite matrix which describes the relationships between the outputs.
Fukumizu, K., Dinuzzo, F.
core
Structured low rank tracker with smoothed regularization
, 2016 In this paper, we propose a structured low rank learning algorithm with smoothed regularization for robust object tracking, under particle filter framework.
Li XM(李小毛) +3 more
core
Deep learning through sparse and low-rank modeling /
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better ...
Huang, Thomas S.editor. +2 more
core
A dual framework for low-rank tensor completion
, 2018 One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization. However, most existing works in this direction learn a sparse combination of tensors.
Jawanpuria, Pratik +2 more
core
, 2016 Explore the convergence between a given function and its multipoint Padé approximants. The multipoint Padé approximants are calculated for given knot points by using a continued fraction.
Radzimirski, Blazej
core
Low-Rank Positive Approximants of Symmetric Matrices ()
Advances in Linear Algebra & Matrix Theory, 2014 Given a symmetric matrix X, we consider the problem of finding a low-rank positive approximant of X. That is, a symmetric positive semidefinite matrix, S, whose rank is smaller than a given positive integer, , which is nearest to X in a certain matrix norm. The problem is first solved with regard to four common norms: The Frobenius norm, the Schatten p-
Achiya Dax
exaly +2 more sources
Multipoint Rational Approximants with Preassigned Poles [PDF]
Journal of Mathematical Analysis and Applications, 2001 20 pages, no figures.-- MSC1991 codes: 41A21, 42C05, 30E10.MR#: MR1820073 (2002i:41021)Zbl#: Zbl 1160.41305Let $\mu$ be a finite positive Borel measure whose support $S(\mu)$ is a compact regular set contained in $\Bbb R$. For a function of Markov type $\
Guillermo Tomás López-Lagomasino
exaly +2 more sources
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On Best Low Rank Approximation of Positive Definite Tensors
SIAM Journal on Matrix Analysis and Applications, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eric Evert, Lieven De Lathauwer
openaire +3 more sources
2020 IEEE International Conference on Multimedia and Expo (ICME), 2020
As an effective descriptor, Symmetric Positive Definite (SPD) matrix is widely used in several areas such as image clustering. Recently, researchers proposed some effective methods based on low rank theory for SPD data clustering with nonlinear metric.
Xinglin Piao +7 more
openaire +1 more source
As an effective descriptor, Symmetric Positive Definite (SPD) matrix is widely used in several areas such as image clustering. Recently, researchers proposed some effective methods based on low rank theory for SPD data clustering with nonlinear metric.
Xinglin Piao +7 more
openaire +1 more source