Results 61 to 70 of about 528,580 (282)
, 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
AAA+ protein unfoldases—the Moirai of the proteome
FEBS Letters, EarlyView.AAA+ unfoldases are essential molecular motors that power protein degradation and disaggregation. This review integrates recent cryo‐electron microscopy (cryo‐EM) structures and single‐molecule biophysical data to reconcile competing models of substrate translocation.
Stavros Azinas, Marta Carroni
wiley +1 more source
, 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
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
Loose L1/2 regularised sparse representation for face recognition
IET Computer Vision, 2015 Sparse representation (or sparse coding) has been applied to deal with frontal face recognition. Two representative methods are the sparse representation‐based classification (SRC) and the collaborative representation‐based classification (CRC), in which
Dexing Zhong +3 more
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
Kernel difference maximisation-based sparse representation for more accurate face recognition
The Journal of Engineering, 2020 Most methods for sparse representation are designed to be used in the original space. However, their performance is not always satisfactory especially when training samples are limited.
Lian Wu +4 more
doaj +1 more source
Weak Signal Detection Based on Combination of Sparse Representation and Singular Value Decomposition
Applied Sciences, 2022 Due to the inevitable acquisition system noise and strong background noise, it is often difficult to detect the features of weak signals. To solve this problem, sparse representation can effectively extract useful information according to the sparse ...
Huijie Ma +3 more
doaj +1 more source
Fast sparse representation with prototypes [PDF]
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010 Sparse representation has found applications in numerous domains and recent developments have been focused on the convex relaxation of the lo-norm minimization for sparse coding (i.e., the l\-norm minimization). Nevertheless, the time and space complexities of these algorithms remain significantly high for large-scale problems.
Jia-Bin Huang 0001, Ming-Hsuan Yang 0001
openaire +1 more source
Molecular Oncology, EarlyView.Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson +9 more
wiley +1 more source