Results 21 to 30 of about 224,871 (275)
Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article ...
Peter Földiák, Dominik Endres
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Frontiers in Computational Neuroscience, 2019 Sparse coding models of natural images and sounds have been able to predict several response properties of neurons in the visual and auditory systems. While the success of these models suggests that the structure they capture is universal across domains ...
Eric McVoy Dodds +4 more
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IEEE Transactions on Information Theory, 2014 51 pages, 12 figures, submitted to IEEE Transactions on Information ...
Shenghao Yang 0001, Raymond W. Yeung
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Sparse representation based intraframe and semi‐intraframe video coding schemes for low bitrates
IET Image Processing, 2021 This paper proposes some extensions of the successful sparse coding of still images to intraframe and semi‐intraframe video coding. The presented frameworks apply the efficient K‐singular value decomposition and recursive least squares dictionary ...
Maziar Irannejad +1 more
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Disaggregating Transform Learning for Non-Intrusive Load Monitoring
IEEE Access, 2018 This paper addresses the problem of energy disaggregation/non-intrusive load monitoring. It introduces a new method based on the transform learning formulation. Several recent techniques, such as discriminative sparse coding, powerlet disaggregation, and
Megha Gaur, Angshul Majumdar
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LOW-DIMENSIONAL STRUCTURES: SPARSE CODING FOR NEURONAL ACTIVITY [PDF]
Journal of Innovative Optical Health Sciences, 2013 Neuronal ensemble activity codes working memory. In this work, we developed a neuronal ensemble sparse coding method, which can effectively reduce the dimension of the neuronal activity and express neural coding.
YUNHUA XU, WENWEN BAI, XIN TIAN
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Fast Convolutional Sparse Coding [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e.
Hilton Bristow +2 more
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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
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Mathematics, 2022 Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC ...
Bing Zhang, Jizhong Liu
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Unsupervised Feature Learning by Deep Sparse Coding [PDF]
, 2013 In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks.
He, Yunlong +4 more
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