Then, a gain scheduling H2/H∞ state feedback controller is designed by solving linear matrix inequalities, which aims to reduce the loading.
Based on this new formulation of the CCA problem, we developed a new sparse CCA based on penalized rank-1 matrix approximation which aims to overcome the drawback of CCA in the context of high-dimensional data and improved interpretability.
Then, MF [2, 3, 4] methods play an important role in model-based CF methods, which aim to learn latent factors on user-item matrix.
Many novel therapeutics have been developed which aim to target this process by acting as either angiogenesis inhibitors (e.g. by blocking the action of vascular endothelial growth factor) or as vascular disrupting agents, which target the extracellular matrix [2],[3].
These approaches are typically based on the definition of coefficient matrices, which are decomposed aiming to obtain the basis vectors.
This paper describes the concept of matrix-structured manufacturing systems (MMS) which aims at providing high operational flexibility and scalability.
The second experiment was coupled with the control experiment, which aimed to compare two matrices of replicated data within a species.
We consider the nonnegative inverse eigenvalue problem with partial eigendata, which aims to find a nonnegative matrix such that it is nearest to a pre-estimated nonnegative matrix and satisfies the prescribed eigendata.
Finally, the optimal forwarding matrix design is formulated as an optimization problem which aims at minimizing the total consumed power at the cognitive relay with prescribed transmission QoS guarantee for destination nodes and interference control for primary users.
The spectral mapping technique uses linear transformations such as, rotation, scaling, etc. on the target matrix, and is modeled through an optimization objective which aims to maximize the similarity between the source and target while preserving their original structure.
