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Journal of Computational Physics, ISSN 0021-9991, 06/2016, Volume 315, pp. 194 - 210
We propose a convex variational approach to compute localized density matrices for both zero temperature and finite temperature cases, by adding an entry-wise... 
[formula omitted] norm | Linear-scaling algorithms | Hamiltonian | Finite temperature | Localized density matrix | norm | l norm | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODEL | PHYSICS, MATHEMATICAL | Specific gravity | Analysis | Algorithms
Journal Article
SIAM Journal on Optimization, ISSN 1052-6234, 2011, Volume 21, Issue 2, pp. 572 - 596
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into... 
Nuclear norm minimization | Semidefinite programming | Sparsity | Uncertainty principle | Convex relaxation | Rank | Matrix decomposition | norm minimization | semidefinite programming | MATHEMATICS, APPLIED | l norm minimization | MINIMIZATION | UNCERTAINTY PRINCIPLES | uncertainty principle | rank | sparsity | matrix decomposition | convex relaxation | nuclear norm minimization
Journal Article
by Liu, FL and Du, RY and Wu, J and Zhou, QP and Zhang, ZX and Cheng, YJ
IEEE SENSORS JOURNAL, ISSN 1530-437X, 08/2018, Volume 18, Issue 15, pp. 6311 - 6318
This paper presents an effective adaptive beamforming method based on an 12-norm minimization problem with multiple constraints, named as MC-l(2)-M algorithm.... 
PERFORMANCE ANALYSIS | MISMATCH | COVARIANCE-MATRIX RECONSTRUCTION | INSTRUMENTS & INSTRUMENTATION | PHYSICS, APPLIED | Array signal processing | l-norm | ROBUST | interference subspace estimation | beamforming | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
ELECTRONICS LETTERS, ISSN 0013-5194, 11/2018, Volume 54, Issue 23, pp. 1346 - 1347
Journal Article
by Hu, HW and Ma, B and Jia, YD
NEUROCOMPUTING, ISSN 0925-2312, 04/2015, Volume 154, pp. 41 - 49
In most object tracking algorithms based on sparse representation, the optimization problem is often formulated as an l(1) or l(2) minimization problem,... 
l-norm minimization | Visual tracking | APPEARANCE MODEL | l(0-)norm minimization | ROBUST | Multi-task | Sparse coding | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | OBJECT TRACKING
Journal Article
SIAM JOURNAL ON SCIENTIFIC COMPUTING, ISSN 1064-8275, 2009, Volume 31, Issue 3, pp. 2047 - 2080
Journal Article
by Yu, WZ and Wang, R and Nie, FP and Wang, F and Yu, Q and Yang, XJ
NEUROCOMPUTING, ISSN 0925-2312, 11/2018, Volume 316, pp. 322 - 331
Locality preserving projection (LPP) is a classical tool for dimensionality reduction and feature extraction. It usually makes use of the l(2)-norm criterion... 
Dimensionality reduction | l-norm minimization | ROBUST FEATURE-EXTRACTION | EXTENSIONS | PRINCIPAL COMPONENT ANALYSIS | Robust | DISCRIMINANT-ANALYSIS | Locality preserving projection (LPP) | CRITERION | L1-NORM MAXIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Journal Article
by Dong, WH and Wu, XJ
NEURAL PROCESSING LETTERS, ISSN 1370-4621, 08/2018, Volume 48, Issue 1, pp. 299 - 312
In the real world, data samples are often contaminated. Using these contaminated data samples for subspace segmentation usually leads to segmentation results... 
Subspace segmentation | MOTION SEGMENTATION | l-norm | ALGORITHM | Nuclear norm | Regularization | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Neurocomputing, ISSN 0925-2312, 03/2018, Volume 283, pp. 228 - 240
High dimension is one of the key characters of big data. Feature selection, as a framework to identify a small subset of illustrative and discriminative... 
ℓ2, p-norm regularization | Feature selection | Capped ℓ2-norm loss | norm loss | Capped ℓ | norm regularization | REGRESSION | RECOGNITION | l(2,p)-norm regularization | CLASSIFICATION | Capped l-norm loss | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Military electronics industry | Analysis
Journal Article
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, ISSN 0888-3270, 05/2014, Volume 46, Issue 1, pp. 59 - 69
The objective of this paper is to provide a new theoretical basis to identify localized damage in structures using incomplete modal information, such as a... 
Structural dynamics | l norm | Sensitivity | Inverse problems | Damage detection | Sparsity | MODAL PARAMETERS | SYSTEMS | REGULARIZATION | STOCHASTIC SUBSPACE IDENTIFICATION | NATURAL FREQUENCIES | ENGINEERING, MECHANICAL
Journal Article
JOURNAL OF SEISMIC EXPLORATION, ISSN 0963-0651, 08/2019, Volume 28, Issue 4, pp. 393 - 411
A new method to estimate the phase of the wavelet when only seismic data is available is presented. Starting from the classical convolutional model of the... 
phase | GEOCHEMISTRY & GEOPHYSICS | FISTA | 2ND-ORDER | l-norm | wavelet | Kurtosis | INVERSION | sparse-deconvolution
Journal Article
INFORMATION SCIENCES, ISSN 0020-0255, 07/2015, Volume 308, pp. 3 - 22
We consider the problem of person identification using gait sequences under normal, carrying bag and different clothing conditions as the main concern. It has... 
RP-based dimensional reduction | FUSION | l-norm | RECOGNITION | COMPUTER SCIENCE, INFORMATION SYSTEMS | ENERGY IMAGE | Individual identification | Covariate factors | Gait recognition | Joint Sparsity Model (JSM)
Journal Article
Journal Article
IEEE Signal Processing Letters, ISSN 1070-9908, 08/2012, Volume 19, Issue 8, pp. 487 - 490
Journal Article
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