fractional Laplacianwave equationsdispersionSeveral wave equations for power-law attenuation have a spatial fractional derivative in the loss term. Both one-sided and two-sided spatial fractional derivatives can give causal solutions and a phase velocity dispersion which satisfies the Kramers–Kronig ...
The Laplacian eigenmaps (LE) is one of the most commonly used nonlinear dimensionality reduction methods and aims to find a low-dimensional representation to preserve the topological relationship between sample points in the original data. However, the 2-norm based loss function makes LE unable to ...
B.H. Sheng, D.H. Xiang, The performance of semi-supervised Laplacian regularized regression with least square loss, Int. J. Wavel. Multiresolut. Inf. Process. 15 (2) (2017) 31. 1750016.B. Sheng and D. Xiang, "The performance of semi-supervised Laplacian regularized regression with the...
In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict ...
Under certain a priori conditions it is shown that the sample mean is an admissible estimator of the location parameter in the case of a Laplacian loss function and a nuisance scale parameter only for samples from a Gaussian population.
The main purpose of this work is to propose the strategy to use the Laplacian smoothing stochastic gradient descent with combination of multiplicative angular margin to enhance the performance of angularly discriminative features of angular margin softmax loss for face recognition. The model is trained...