Results 71 to 80 of about 522,381 (263)

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, Bao-Di Liu, Shuai Shao, Ye Li, Yan-Jiang Wang   +4 more
doaj   +1 more source

Subspace-Sparse Representation

, 2015
15 pages, 3 figures, previous version published in ICML ...
You, C., Vidal, R.
openaire   +2 more sources

Correction: Multiple Sparse Representations Classification

PLOS ONE, 2015
The second author’s name is spelled incorrectly. The correct name is: Stefan Klein. The correct citation is: Plenge E, Klein S, Niessen WJ, Meijering E (2015) Multiple Sparse Representations Classification. PLoS ONE 10(7): e0131968.
Esben Plenge, Stefan Klein, Wiro J Niessen, Erik Meijering   +3 more
openaire   +3 more sources

Fast Kernel Sparse Representation [PDF]

2011 International Conference on Digital Image Computing: Techniques and Applications, 2011
Two efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick.
Hanxi Li, Yongsheng Gao, Jun Sun
openaire   +1 more source

Development of a Prediction Model for Progression Risk in High‐Grade Gliomas Based on Habitat Radiomics and Pathomics

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, Yuxi Gong, Weilin Xu, Xingjian Sun, Gefei Jiang, Lei Qiu, Kexin Shi, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou   +14 more
wiley   +1 more source

Convolutional sparse coding network for sparse seismic time-frequency representation

Artificial Intelligence in Geosciences
Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data.
Qiansheng Wei, Zishuai Li, Haonan Feng, Yueying Jiang, Yang Yang, Zhiguo Wang   +5 more
doaj   +1 more source

Vehicle recognition in acoustic sensor networks using multiple kernel sparse representation over learned dictionaries

International Journal of Distributed Sensor Networks, 2017
Sparse representation–based classification and kernel methods have emerged as important methods for pattern recognition. In this work, we study the problem of vehicle recognition using acoustic sensor networks in real-world applications.
Rui Wang, Wenming Cao, Zhihai He
doaj   +1 more source

Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Progressive Supranuclear Palsy (PSP) is a neurodegenerative ‘tauopathy’ with predominating pathology in the basal ganglia and midbrain. Caudal tau spread frequently implicates the cerebellum; however, the pattern of atrophy remains equivocal.
Chloe Spiegel, Timothy P. Siejka, Cassandra Marotta, Josh J. Y. Lee, Kelly Bertram, Terence J. O'Brien, Meng Law, Lucy Vivash, Ian H. Harding   +8 more
wiley   +1 more source

Dictionary Learning of Convolved Signals [PDF]

, 2013
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation of them.
Barchiesi, Daniele, Plumbley, Mark
core   +1 more source

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source