Results 1 to 10 of about 522,381 (263)
Application of Sparse Representation in Bioinformatics [PDF]
Frontiers in Genetics, 2021 Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority.
Shuguang Han +8 more
doaj +2 more sources
A Hybrid Sparse Representation Model for Image Restoration [PDF]
Sensors, 2022 Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation.
Caiyue Zhou +5 more
doaj +2 more sources
A Survey of Sparse Representation: Algorithms and Applications
IEEE Access, 2015 Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition.
Zheng Zhang +4 more
doaj +5 more sources
OPTIMIZATION OF THE SPARSE REPRESENTATION PARAMETERS FOR THE FUSION OF REMOTELY SENSED SATELLITE IMAGES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Image fusion methods are widely used in remote sensing applications to obtain more information about the features in the study area. One of the recent satellite image fusion techniques that can deal with noise and reduce computational cost and deal with ...
A. Asefpour Vakilian, M. R. Saradjian
doaj +1 more source
Collaborative and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Sparse representation can be used for the representation of high-dimensional data into a low-dimensional subspace. Recently, sparse graph-based discriminant analysis that uses l1-norm optimization has drawn much attention in dimensionality reduction of ...
Chiranjibi Shah, Qian Du
doaj +1 more source
Face Recognition Based on Robust Principal Component Analysis and Kernel Sparse Representation [PDF]
Jisuanji gongcheng, 2016 Aiming at the problems that the existing face recognition methods are hard to efficiently overcome the effect of noise and error disturbance (such as illumination,occlusion,and face expression).Kernel sparse representation classification based on Robust ...
LIAO Ruihua,LI Yongfan,LIU Hong
doaj +1 more source
IEEE Access, 2020 Multitask sparse representation method improves the detection performance by constructing multiple associated sub-sparse representation tasks and jointly learning multiple sub-sparse representation tasks, and this method can make use of the spectral ...
Xianfeng Ou +6 more
doaj +1 more source
Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
Applied Sciences, 2021 Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area.
Kyuchang Kang, Changseok Bae
doaj +1 more source
A novel sparse representation algorithm for AIS real-time signals
EURASIP Journal on Wireless Communications and Networking, 2018 Sparse representation of signals based on a redundant dictionary is a new signal representation theory. Recent research activities in this field have concentrated mainly on the study of dictionary design and sparse decomposition algorithms.
Shuaiheng Huai, Shufang Zhang
doaj +1 more source
The Sparsity Gap: Uncertainty Principles Proportional to Dimension [PDF]
, 2010 In an incoherent dictionary, most signals that admit a sparse representation admit a unique sparse representation. In other words, there is no way to express the signal without using strictly more atoms.
Tropp, Joel A.
core +3 more sources