Sentence examples for matrix approximation problem from inspiring English sources

In this section, we use the SVT algorithm for the low-rank matrix approximation problem.

Finally, we use the SVT algorithm for the low-rank matrix approximation problem.

Therefore, it is necessary and significant to study the lower bound of the low-rank matrix approximation problem.

There are some reasons for the study of lower bound of a low-rank matrix approximation problem.

We use the connection of PCA with singular value decomposition (SVD) of the data matrix and extract the PCs through solving a low rank matrix approximation problem.

In the low rank matrix approximation problem, the well known nuclear norm minimization (NNM) problem plays a crucial role and attracts significant interests in recent years.

Desired matrix properties including satisfaction of characteristic equation, symmetry, positive semidefiniteness, and sparsity are imposed as side constraints to form the optimal matrix pencil approximation problem.

In this paper, an iterative algorithm is constructed to solve the general coupled matrix equations and their optimal approximation problem over generalized reflexive matrix solution ( X 1, X 2, …, X q ).

In this section, we will propose the sparse CCA method based on rank-1 matrix approximation by penalizing the optimization problem (18).

However, in the problem of low-rank matrix approximation, (|D|) is not necessarily equal to (|A|), so the approximation error is present.

In this work, we develop a novel and efficient rank-one matrix approximation method, named QMA, to address the problem of detecting accurately also the positive end of interactions, yet being simple enough for the large-data interaction datasets.