sparsity's Usage Examples:

for an m × n matrix) is sometimes referred to as the sparsity of the matrix.

Conceptually, sparsity corresponds to systems with few pairwise interactions.

w j {\displaystyle f(x)=\sum _{j=1}^{p}\phi _{j}(x)w_{j}} Enforcing a sparsity constraint on w {\displaystyle w} can lead to simpler and more interpretable.

are the 80/20 rule, the law of the vital few, or the principle of factor sparsity.

Efficiency is obtained by exploiting the sparsity structures of the Jacobian and of the Hessian.

Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization.

representations of the same signal but also provide an improvement in sparsity and flexibility of the representation.

This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than.

It handles the sparsity of the original matrix better than memory based ones.

Other graph families not characterized by their sparsity can also be described in this way.

the statistical analysis of the results from factorial experiments, the sparsity-of-effects principle states that a system is usually dominated by main.

As a result, there is no formal sense of sparsity for any finite network, despite widespread agreement that most empirical.

The purpose of regularization is to enforce conditions, for example sparsity or smoothness, that can produce stable predictive functions.

(SKA) the sparsity of the adder refers to how many carry bits are generated by the carry-tree.

Generating every carry bit is called sparsity-1, whereas.

respect to the sparsity pattern S {\displaystyle S} ).

The sparsity pattern of L and U is often chosen to be the same as the sparsity pattern of the original.

It may be Tucanoan, but more recently has been left unclassified due to sparsity of data.

This property of multi-dimensional spaces is referred to as sparsity.

When representations are learned in a way that encourages sparsity, improved performance is obtained on classification tasks.

McKee, is an algorithm to permute a sparse matrix that has a symmetric sparsity pattern into a band matrix form with a small bandwidth.

Greek ὀλίγος (olígos, "few") and καινός (kainós, "new"), and refers to the sparsity of extant forms of molluscs.

Synonyms:

thinness; exiguity; spareness; meagerness; scantiness; scantness; poorness; leanness; meagreness; sparseness;

Antonyms:

fruitfulness; wealth; fatness; sufficiency; adequacy;