Results 61 to 70 of about 523,433 (260)
, 2010 As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact ...
Dong, Weisheng +3 more
core +1 more source
Spiralet Sparse Representation
, 2014 10 pages, Working Paper Number: WP-RFM-14 ...
Moghaddam, Reza Farrahi +1 more
openaire +2 more sources
Patterns of Postictal Abnormalities in Relation to Status Epilepticus in Adults
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Abnormalities on peri‐ictal diffusion‐weighted magnetic resonance imaging (DWI‐PMAs) are well‐established for patients with status epilepticus (SE), but knowledge on patterns of DWI‐PMAs and their prognostic impact is sparse. Methods This systematic review and individual participant data meta‐analysis included observational studies ...
Andrea Enerstad Bolle +11 more
wiley +1 more source
Parametric Sparse Representation and Its Applications to Radar Sensing
Leida xuebao, 2016 Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally ...
Li Gang, Xia Xiang-Gen
doaj +1 more source
, 2015 Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction ...
Nasrabadi, Nasser M. +2 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu +14 more
wiley +1 more source
Analysis for sparse channel representation based on dictionary learning in massive MIMO systems
IET CommunicationsThe accuracy analysis of dictionary sparse representation for channels in massive MIMO systems is a relatively unexplored field. Existing research has primarily focused on investigating the accuracy of dictionary sparse representation using simulation in
Qing‐Yang Guan
doaj +1 more source
Sparse Representation and Collaborative Representation? Both Help Image Classification
IEEE Access, 2019 Image classification has attracted more and more attention. During the past decades, image classification has shown growing interest in representation-based classification methods, such as sparse representation-based classification and collaborative ...
Wen-Yang Xie +4 more
doaj +1 more source
Information-theoretically Optimal Sparse PCA
, 2014 Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations
Deshpande, Yash, Montanari, Andrea
core +1 more source
Subspace-Sparse Representation
, 2015 15 pages, 3 figures, previous version published in ICML ...
You, C., Vidal, R.
openaire +2 more sources