Results 31 to 40 of about 965,256 (248)
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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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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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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An Optimum Deraining Scheme using Sparse Coding
International Journal of Emerging Research in Engineering, Science, and Management, 2022 Rain streak removal is a challenging and interesting task of image processing where the rain streaks will be removed from an image with rain streaks. In the literature, a large number of proposals are made where rain streak removal is considered as image
A Hazarathaiah +4 more
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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
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Representation Learning via Cauchy Convolutional Sparse Coding
IEEE Access, 2021 In representation learning, Convolutional Sparse Coding (CSC) enables unsupervised learning of features by jointly optimising both an $\ell _{2}$ -norm fidelity term and a sparsity enforcing penalty.
Perla Mayo +3 more
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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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Sparse representation of salient regions for no-reference image quality assessment
International Journal of Advanced Robotic Systems, 2016 This paper introduces an efficient feature learning framework via sparse coding for no-reference image quality assessment. The important part of the proposed framework is based on sparse feature extraction from a sparse representation matrix, which is ...
Tianpeng Feng +5 more
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Local structure preserving sparse coding for infrared target recognition. [PDF]
PLoS ONE, 2017 Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex.
Jing Han +3 more
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Parametric dictionary design for sparse coding [PDF]
, 2009 —This paper introduces a new dictionary design method for sparse coding of a class of signals. It has been shown that one can sparsely approximate some natural signals using an overcomplete set of parametric functions, e.g. [1], [2].
Yaghoobi, M. +5 more
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