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In this letter we explore an alternative structural representation for Steinbuch-type binary associative memories. These networks offer very generous storage capacities (both asy...
Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse lin...
Recently, sparse coding has been receiving much attention in object and scene recognition tasks because of its superiority in learning an effective codebook over k-means clusterin...
While evidence indicates that neural systems may be employing sparse approximations to represent sensed stimuli, the mechanisms underlying this ability are not understood. We desc...
Christopher J. Rozell, Don H. Johnson, Richard G. ...
Image representation has been a key issue in vision research for many years. In order to represent various local image patterns or objects effectively, it is important to study th...
Jun Miao, Lijuan Duan, Laiyun Qing, Wen Gao, Xilin...
A new dictionary selection approach for sparse coding, called parametric dictionary design, has recently been introduced. The aim is to choose a dictionary from a class of admissi...
Mehrdad Yaghoobi, Laurent Daudet, Michael E. Davie...
Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex [1]. Ho...
William K. Coulter, Cristopher J. Hillar, Guy Isle...
In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a ...
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...