Search results
Results from the WOW.Com Content Network
Each iteration of the Sierpinski triangle contains triangles related to the next iteration by a scale factor of 1/2. In affine geometry, uniform scaling (or isotropic scaling [1]) is a linear transformation that enlarges (increases) or shrinks (diminishes) objects by a scale factor that is the same in all directions (isotropically).
A manifold is isotropic if the geometry on the manifold is the same regardless of direction. A similar concept is homogeneity. Isotropic quadratic form A quadratic form q is said to be isotropic if there is a non-zero vector v such that q(v) = 0; such a v is an isotropic vector or null vector.
Another application is nonmetric multidimensional scaling, [1] where a low-dimensional embedding for data points is sought such that order of distances between points in the embedding matches order of dissimilarity between points. Isotonic regression is used iteratively to fit ideal distances to preserve relative dissimilarity order.
In probability theory, an isotropic measure is any mathematical measure that is invariant under linear isometries. It is a standard simplification and assumption used in probability theory. It is a standard simplification and assumption used in probability theory.
Both are isotropic forms of discrete Laplacian, [8] and in the limit of small Δx, they all become equivalent, [11] as Oono-Puri being described as the optimally isotropic form of discretization, [8] displaying reduced overall error, [2] and Patra-Karttunen having been systematically derived by imposing conditions of rotational invariance, [9 ...
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. [1] It has been used in many fields including econometrics, chemistry, and engineering. [2]
In this method, a 'big' covariance is constructed, which describes the correlations between all the input and output variables taken in N points in the desired domain. [24] This approach was elaborated in detail for the matrix-valued Gaussian processes and generalised to processes with 'heavier tails' like Student-t processes .
Developed on the basis of the super-resolution generative adversarial network (SRGAN) method, [8] enhanced SRGAN (ESRGAN) [9] is an incremental tweaking of the same generative adversarial network basis. Both methods rely on a perceptual loss function [10] to evaluate training iterations.