subspace

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Related to subspaces: Orthogonal subspaces
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  • noun

Words related to subspace

a space that is contained within another space

Based on WordNet 3.0, Farlex clipart collection. © 2003-2012 Princeton University, Farlex Inc.
References in periodicals archive ?
From (10) it follows that the subspaces [H.sub.k], k = 1,...
They circumvent one drawback of solvers based on Krylov subspaces, namely that the subspace dimension grows with each iteration of the method, at least if no restarts are used.
Over several decades, this topic has been extensively studied and varieties of techniques including multiple signal classification (MUSIC) [7], minimum norm (MN) [8], estimation of the signal parameters via rotational invariance techniques (ESPRIT) [9], subspace fitting (SF) [10], and maximum likelihood (ML) [11] have been developed.
Hence the dual continuous map [h.sup.**] of [h.sup.*] from the space Ck([C.sub.k](X)) to Ck([C.sub.k](Y)) is an embedding of Ck([C.sub.k](X)) into Ck([C.sub.k](Y)) as a locally convex subspace. Observe that since X and Y are [k.sub.[omega]]-spaces, L(X) and L(Y) are locally convex subspaces of [C.sub.k]([C.sub.k](X)) and [C.sub.k]([C.sub.k](Y)), respectively, by [10,14] (for a more general assertion, see Theorem 1.2 of [15]).
Svirshchevskii, Exact Solutions And Invariant Subspaces of Nonlinear Partial Differential Equations in Mechanics And Physics, Chapman & Hall/CRC, Chapman & Hall/CRC, London, UK, 2007.
be regular subspaces of a Krein space H such that [N.sub.1] [contains] [N.sub.2] [contains] ....
For an input image, IniSeedList classifies each pixel into one of the eight subspaces according to its minimum distance to vertex i that is a well-defined color in the RGB space.
Therefore, we present statistical tests examining the biases of NE and MI and the effects of the sampling and dimensionality, which helps in finding robust non-Gaussian subspaces.
On the other hand, every reflexive subspace H of a Banach space X is proximinal; see Corollary 2.5 in [5]; hence a Banach space is always weakly symmetric with respect to its reflexive subspaces.
This algorithm fully uses the known category information of training samples and seeks the projection directional subspaces which must be the most useful to classification.
Garkavi obtained the following characterization for finite co-dimensional proximinal subspaces of C(K).