Sentence examples for matrix of latent from inspiring English sources

When matrix factorization is used in machine learning, the adjacency matrix A is given and the goal is to train the model by tuning the matrix of latent features V in such way that (A approx V^T V).

Table 8 Correlation matrix of latent constructs   QPI LP ST NE QPI 1.000 LP 0.674 1.000 ST 0.337 0.591 1.000 NE 0.578 0.731 0.359 1.000 0.359 1.000 QPI quality of port infrastructure, LP logistics performance, ST seaborne trade, NE national economy.

PLS, then, can be seen as a method to construct a matrix of latent variables as a linear transformation of X, where W*(p × a) is a matrix of weights.

Although a naive analysis would treat the error matrix as having independent rows, an alternative model is to assume a factor-analytic structure on or, specifically (3) where is an matrix of CpG-specific factor loadings, is an matrix of latent effects and is an matrix of 'uniqueness' errors.

Thus, g(x i ) can be expressed as: where t a is latent component a (a = 1, 2..., h) and generally h << p. PLSR is similar to the well-known principal component regression (PCR), both methods construct a matrix of latent components T as a linear transformation of X, T = XW, where W is a matrix of weights.

Theoretically, NMF can be treated as a matrix version of latent factor analysis.

In [6] it is solved via matrix factorization: a network adjacency matrix A is approximated by a product of the matrices V and (V^T), where V is the matrix of nodes' latent features.

The main idea is to approximate a network adjacency matrix by a product of matrices V and (V^T), where V is the matrix of nodes' latent features vectors.

and to minimize (4) with respect to A and D. Note that, in the field of ICA, only the loading matrix A is of interest since it corresponds to the mixing matrix of several latent source signals.

With regard to covariance matrices, in the LMM the covariance depends on the values of time and the covariance matrix of the latent intercept and slope.

Similar, denote a N-by N matrix E(i ) as the weight matrix of ith latent graph, we define another n'-by n' weight matrix Ē(i ) which only respects the linkage weights among the labeled examples of the ith latent graph.