… A predictive spatial model which works well for areas where vegetation is sparse, perhaps
… Thus the second of these ways of dividing up the data is required, to be able to generalize …

… sparse … the generalized curve grid is not limited to orthogonal and its generation is very
convenient. And topical landform and roughness modification is adopted for project generalization…

… can be generalized into regularized sparse optimization models to solve the sparse solutions
of underdetermined systems. Therefore, the improvement of such models and the design of …

… Intheneighborhoodselec⁃ tion framework, we use the L1 and L2 regularizers to incorporate
the sparse and group prior of the hub network, so as to make the network easier to generate …

… generalized inverse Gaussian distribution, we adopt the generalized hyperbolic distribution
to model … construct the Gaussian-generalized-hyperbolic mixing distribution to model the non-…

… Two typical intensity fluctuation models are selected as theoretical channel models. The
probability density function (PDF) of the selected model obeys exponential generalized Gamma …

… This paper summarizes the progress from four aspects: sparse matrix optimization problem,
low rank … For the sparse matrix optimization problem, we mainly focus on the sparse inverse …

… ; in the decision phase, three model selection criteria and the Possibility Matrix method are
proposed to aid decision makers to select the preferable solutions through differentiating the …

… very sparse, we propose a semisupervised sparse multilinear … of our method to extract
sparse and effective features that … Generalized low rank approximations of matrices (GLRAM)[15] …

… be able to build up over time and possibly remains sparse. On the other hand, the network
would … can be called a generalized gossip algorithm with θ as the generalizing parameter. The …