Directional Wind Spectrum Description using Bivariate L1 Norm RBFs
نویسندگان
چکیده
منابع مشابه
gravity data inversion using l1-norm stabilizer
in this paper the inversion of gravity data using l1–norm stabilizer is considered. the inversion is an important step in the interpretation of data. in gravity data inversion, the goal is to estimate density and geometry of the unknown subsurface model from a set of known observation measured on the surface. commonly, rectangular prisms are used to model the subsurface under the survey area. t...
متن کاملRobust subspace computation using L1 norm
Linear subspace has many important applications in computer vision, such as structure from motion, motion estimation, layer extraction, object recognition, and object tracking. Singular Value Decomposition (SVD) algorithm is a standard technique to compute the subspace from the input data. The SVD algorithm, however, is sensitive to outliers as it uses L2 norm metric, and it can not handle miss...
متن کاملRobust Endmember detection using L1 norm factorization
Given this model, spectral unmixing and endmember detection are the tasks of determining the endmembers and the proportions for every data point in the scene. Several endmember detection and spectral unmixing algorithms have been developed in the literature. However, the majority of these methods do not provide an autonomous way to estimate the number of endmembers and, thus, require the number...
متن کاملL1-norm Kernel PCA
We present the first model and algorithm for L1-norm kernel PCA. While L2-norm kernel PCA has been widely studied, there has been no work on L1-norm kernel PCA. For this non-convex and non-smooth problem, we offer geometric understandings through reformulations and present an efficient algorithm where the kernel trick is applicable. To attest the efficiency of the algorithm, we provide a conver...
متن کاملL1-Norm Quantile Regression
Classical regression methods have focused mainly on estimating conditional mean functions. In recent years, however, quantile regression has emerged as a comprehensive approach to the statistical analysis of response models. In this article we consider the L1-norm (LASSO) regularized quantile regression (L1-norm QR), which uses the sum of the absolute values of the coefficients as the penalty. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International journal for engineering modelling
سال: 2023
ISSN: ['1330-1365', '1849-8671']
DOI: https://doi.org/10.31534/engmod.2023.1.ri.07f