NN
2011
Springer
12 years 11 months ago
2011
Springer
In this letter we explore an alternative structural representation for Steinbuch-type binary associative memories. These networks offer very generous storage capacities (both asy...
CVPR
2011
IEEE
13 years 13 days ago
2011
IEEE
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...
BMVC
2010
13 years 2 months ago
2010
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...
MVA
2007
13 years 3 months ago
2007
Neural networks in the visual system may be performing sparse coding of learnt local features that are qualitatively
NECO
2008
13 years 4 months ago
2008
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...
IJON
2008
13 years 4 months ago
2008
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...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
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...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex [1]. Ho...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
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 ...
ICA
2010
Springer
13 years 5 months ago
2010
Springer
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...