Explore: Sparse Representation

Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input

Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding

In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. There is no strict

"Sparse odor representation and olfactory learning". Nat Neurosci. 11 (10): 1177–1184. doi:10.1038/nn.2192. PMC 3124899. PMID 18794840. A sparse memory

classification error, the representation error, an L1 regularization on the representing weights for each data point (to enable sparse representation of data), and

a sparse representation in Θ ( X ) {\displaystyle {\bf {{\Theta }({\textbf {X}})}}} . This then becomes an optimization problem in finding a sparse Ξ

The convolutional sparse coding paradigm is an extension of the global sparse coding model, in which a redundant dictionary is modeled as a concatenation

like matching pursuit and basis pursuit can recover a signal’s sparse representation from a collection with extra building blocks, known as an overcomplete

the L1 sparse autoencoder) or the L2 norm (giving the L2 sparse autoencoder). Denoising autoencoders (DAE) try to achieve a good representation by changing

learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning