Results 21 to 30 of about 223,248 (273)
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
doaj +1 more source
Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article/Sparse_coding Mammalian brains consist of billions of neurons, each capable of independent electrical activity. Information in the brain is represented by the pattern of activation of this large neural population, forming a neural code.
Foldiak, P, Endres, D M
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Sparse Regression Codes for Multi-terminal Source and Channel Coding [PDF]
, 2012 We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regression and are called Sparse Superposition or Sparse Regression codes.
Tatikonda, Sekhar, Venkataramanan, Ramji
core +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
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
doaj +1 more source
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
core +2 more sources
A weighted block cooperative sparse representation algorithm based on visual saliency dictionary
CAAI Transactions on Intelligence Technology, 2023 Unconstrained face images are interfered by many factors such as illumination, posture, expression, occlusion, age, accessories and so on, resulting in the randomness of the noise pollution implied in the original samples.
Rui Chen +4 more
doaj +1 more source