A kernel-based block matrix decomposition approach for the classification of remotely sensed images was proposed by Gao et al. [4].
Then, a controller is designed such that the class of stochastic nonlinear dynamics are stochastically finite-time bounded and have an H∞ attention performance level by utilizing matrix decomposition approach.
The direct solver is based on a matrix decomposition approach which reduces multi-dimensional separable problems to a sequence of one-dimensional problems that can be efficiently handled by a static condensation process.
For example, in [11], the authors propose a new criterion for sparse CCA and applied a penalized matrix decomposition approach to solve the sparse CCA problem, and in [10], the presented sparse CCA approach computes the canonical projectors from primal and dual representations.
In order to apply the TF matrix decomposition approach for classification of overlapping information, and data retrieval applications, NMF is separately applied on the TF data of each class (i.e., V 1 and V 2 in Fig. 1 a) to decompose class-specific TF bases (i.e., W 1 and W 2) and create an overall TF basis matrix, consisting of all the class-specific TF bases.
Under the assumption of full channel state information (CSI) at the transmit side as well as at the receive side, matrix theory, including several decomposition approaches, is often used as a basic tool to facilitate the precoder design and performance analysis for different types of communication scenarios.
With the aid of matrix decomposition and Kronecker product approach, a fast and efficient algorithm for computing the proposed PROJT is obtained.
Note that unlike weight matrix decomposition and node pruning [40] approaches, soft target training is considerably more flexible because the input feature type and NN architecture are not restricted to be the same as the teacher.
The matrix singular value decomposition approach is then employed to compute the higher order perturbations of eigensolutions.
In QR decomposition approaches, the Q matrix is implicitly formed and then used to compute the R matrix, whereas in the Gram-Schmidt approach, the inverse of the R is implicitly formed and then used to compute the Q matrix.
