Quantification, i.e., the task of predicting the class prevalence values in bags of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms for binary and multi-class problems in which the classes are not...
[translate] a수업 研究[translate] a堆放两层 Piles up two[translate] a屋里没有任何迹象显示有人爱她 正在翻译,请等待...[translate] athis is the first generalization of regularization–based methods 这是基于经常化的方法的第一概念化[translate]...
In particular, two systematic kernel design methods (one is from a machine learning perspective and the other one is from a system theory perspective) were developed in Chen (2018b) by embedding the corresponding type of prior knowledge. The hyperparameter estimation plays a similar role as the...
Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition. High-dimension data are usually assumed to be sampled from the submanifold embedded in the original high-dimension space. To preserve the locality ...
Matrix factorization-based methods approximate the latent HR-HSI using a dictionary and its corresponding coefficients, assuming that every spectral component is able to be expressed as a composition of a small set of distinct spectral patterns. Within this theoretical framework, the primary focus cente...
In the following we will briefly introduce the basic principles of these two methods. The GCV method was first proposed by Golub et al. [30]. It is based on the idea that if an arbitrary element of the left-hand side U (Equation (19)) is left out, then the corresponding regularized ...
An optimization problem is said to be stable, or well posed, if for a small perturbation of the coefficients we obtain small variations of the solutions. Unstable or 'ill-posed' problems need regularizing treatment to make them more stable. In this paper three regularization methods are studied...
These aforementioned methods are\ell_{1}-norm based SR, as a relaxation of\ell_{0}-norm, which unavoidably produce biased estimations and underestimate the true solution [27,28,29]. Then non-convex penalties are getting more and more attention in place of\ell_{1}-norm to improve the sp...
Finally, a large number of numerical experiments show that our methods provide accurate and fast restorations when compared with the state of the art.doi:10.1137/140976261Yuantao CaiMarco DonatelliDavide BianchiTingZhu HuangMathematicsYuantao Cai, Marco Donatelli, Davide Bianchi, and Ting-Zhu Huang....
The effect of PEPR method is also studied with phantoms with different configurations and with different current injection methods. All the resistivity images reconstructed with PEPR method are compared with the single step regularization (STR) and Modified Levenberg Regularization (LMR) techniques. The...