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Sparse approximation ideas and algorithms have been extensively used in signal processing, image processing, machine learning, medical imaging, array processing, data mining, and more. In most of these applications, the unknown signal of interest is modeled as a sparse combination of a few atoms from a given dictionary, and this is used as the ...
In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. [1] There is no strict definition regarding the proportion of zero-value elements for a matrix to qualify as sparse but a common criterion is that the number of non-zero elements is roughly equal to the number of ...
A simple unweighted network of size is called sparse if the number of links in it is much smaller than the maximum possible number of links : [1] = (). In any given (real) network, the number of nodes N and links M are just two numbers, therefore the meaning of the much smaller sign (above) is purely colloquial and informal, and so are statements like "many real networks are sparse."
However, if there is a unique sparse solution to the underdetermined system, then the compressed sensing framework allows the recovery of that solution. Solution / reconstruction method Example of the retrieval of an unknown signal (gray line) from few measurements (black dots) using the knowledge that the signal is sparse in the Hermite ...
The opposite, a graph with only a few edges, is a sparse graph. The distinction of what constitutes a dense or sparse graph is ill-defined, and is often represented by 'roughly equal to' statements. The distinction of what constitutes a dense or sparse graph is ill-defined, and is often represented by 'roughly equal to' statements.
Sparse may refer to: Sparse, a software static analysis tool; Sparse language, a type of formal language in computational complexity theory; Sparse matrix, in numerical analysis, a matrix populated primarily with zeros; Sparse file, a computer file mostly empty; Sparse network, a network with many fewer connections than possible
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 data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.
Disadvantages are that sparse files may become fragmented; file system free space reports may be misleading; filling up file systems containing sparse files can have unexpected effects (such as disk-full or quota-exceeded errors when merely overwriting an existing portion of a file that happened to have been sparse); and copying a sparse file with a program that does not explicitly support ...