Results 51 to 60 of about 528,580 (282)
Sparse representations for text categorization [PDF]
Interspeech 2010, 2010 Sparse representations (SRs) are often used to characterize a test signal using few support training examples, and allow the number of supports to be adapted to the specific signal being categorized. Given the good performance of SRs compared to other classifiers for both image classification and phonetic classification, in this paper, we extended the ...
Tara N. Sainath +5 more
openaire +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Gastrointestinal graft‐versus‐host disease (GI GVHD) following hematopoietic stem cell transplant is typically managed with medical therapy, but surgery and angioembolization may be warranted in selected cases with life‐threatening complications.
Gaia Brunetti +12 more
wiley +1 more source
Integrated Sparse Coding With Graph Learning for Robust Data Representation
IEEE Access, 2020 Sparse coding is a popular technique for achieving compact data representation and has been used in many applications. However, the instability issue often causes degeneration in practice and thus attracts a lot of studies.
Yupei Zhang, Shuhui Liu
doaj +1 more source
Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal ...
Hailin Wang +5 more
doaj +1 more source
Sparse Representation-based Open Set Recognition
, 2017 We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification.
Patel, Vishal M., Zhang, He
core +1 more source
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification
Remote Sensing, 2017 Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification.
Fei Tong +3 more
doaj +1 more source
Sparse Representation-based Image Quality Assessment
, 2013 A successful approach to image quality assessment involves comparing the structural information between a distorted and its reference image. However, extracting structural information that is perceptually important to our visual system is a challenging ...
Guha, Tanaya +2 more
core +1 more source
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
Remote Sensing, 2020 In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation
Xiaobin Zhao +4 more
doaj +1 more source