Sentence examples for of projection matrix from inspiring English sources

Figure 4(a) shows an example of projection matrix in spectrotemporal domain.

In addition, an extension of projection matrix design is derived for the scenarios where the signals cannot be represented exactly sparse in a specified dictionary.

This phenomenon complicates any standard, successive-stage, life cycle graph to a digraph defined on a 2D lattice in the age and stage dimensions, the pattern of projection matrix becoming more complex too.

Figure 3 Tensor model for calculation of projection matrices via NTPCA.

The contributions of the exterior and interior walls are removed from the data through the use of projection matrices, which are determined from wall- and corner-specific dictionaries.

By using the relationship between orthogonal arrays and decompositions of projection matrices and projection matrix inequalities, we present a method for constructing a class of new orthogonal arrays which have higher percent saturations.

Schoen (2014), building on the birth-death model in Schoen (2013), presented a multistate projection approach based on the idea of "intrinsic linkages," i.e., linear relationships between the dominant right eigenvectors of projection matrices and the sequence of population compositions.

Constructions of OAs have been studied extensively in the literature; see Hedayet et al. [1, 2], Zhang et al. [3, 7], Pang et al. [5], Pang [6], Zhang et al. [7], Chen et al. [8], Du et al. [9], etc. Zhang et al. [3] present a method of construction of orthogonal arrays of strength two by using a relationship between orthogonal arrays and decompositions of projection matrices.

h kk [ n ] w k, al [ n ] 2 = h kk [ n ] Π k ⊥ n − D f + b h ̂ kk H n − D f Π k ⊥ n − D f + b h ̂ kk H n − D f 2 ≈ a ρ l 2 1 − Z kk h kk n − D f 2 Π k ⊥ n − D f + b h ̂ kk H n − D f 2. (a) is obtained using the approximation for h kk [ n] and using the symmetry and squared property of projection matrices.

Recently, concerns have emerged regarding an over-reliance on stable, asymptotic properties of projection matrices.

Hitherto, likelihood methods have largely been neglected for exposing the uncertainty associated with the output of projection matrices.