Results 11 to 20 of about 57,147 (259)
Image Classification Based on Neighborhood Preserving Embedding Sparse Coding [PDF]
Jisuanji gongcheng, 2016 Aiming at the problem of image classification with a complex background,this paper proposes a new image algorithm based on Neighborhood Preserving Embedding regularization Sparse Coding algorithm(NPESC).Comparing with traditional sparse coding,it adds ...
GAO Jiaxue,CHEN Xiuhong
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Image Classification Method Based on Non-negative Elastic Net Sparse Coding Algorithm [PDF]
Jisuanji gongcheng, 2017 In order to improve the image classification accuracy,this paper proposes a Non-negative Elastic Net Sparse Coding(NENSC)algorithm.This algorithm combines the advantages of non-negative sparse coding and elastic net algorithm.It introduces an l2norm ...
ZHANG Yong,ZHANG Yangyang,CHENG Hong,ZHANG Yanxia
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Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization
Applied Sciences, 2022 Sparse orthonormal transform is based on orthogonal sparse coding, which is relatively fast and suitable in image compression such as analytic transforms with better performance.
Gihwan Lee, Yoonsik Choe
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Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. [PDF]
PLoS Computational Biology, 2013 The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive
Joel Zylberberg, Michael Robert DeWeese
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Efficient sparse coding in early sensory processing: lessons from signal recovery. [PDF]
PLoS Computational Biology, 2012 Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as ...
András Lörincz +2 more
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Foundations and Trends® in Communications and Information Theory, 2019 Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparsegraph codes, and polar codes.
Ramji Venkataramanan +2 more
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A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval. [PDF]
PLoS ONE, 2015 Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive
Yunchao Zhang +3 more
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Sparse Coding and Autoencoders [PDF]
2018 IEEE International Symposium on Information Theory (ISIT), 2018 In this new version of the paper with a small change in the distributional assumptions we are actually able to prove the asymptotic criticality of a neighbourhood of the ground truth dictionary for even just the standard squared loss of the ReLU autoencoder (unlike the regularized loss in the older version)
Akshay Rangamani +6 more
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Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article ...
Peter Földiák, Dominik M. 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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