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In the field of statistical learning theory, matrix regularization generalizes notions of vector regularization to cases where the object to be learned is a matrix. The purpose of regularization is to enforce conditions, for example sparsity or smoothness, that can produce stable predictive functions.
Also known as Tikhonov regularization, named for Andrey Tikhonov, it is a method of regularization of ill-posed problems. [ a ] It is particularly useful to mitigate the problem of multicollinearity in linear regression , which commonly occurs in models with large numbers of parameters. [ 3 ]
In probability theory and directional statistics, the von Mises distribution (also known as the circular normal distribution or the Tikhonov distribution) is a continuous probability distribution on the circle. It is a close approximation to the wrapped normal distribution, which is the circular analogue of the normal distribution.
The learning problem with the least squares loss function and Tikhonov regularization can be solved analytically. Written in matrix form, the optimal w {\displaystyle w} is the one for which the gradient of the loss function with respect to w {\displaystyle w} is 0.
The connection between the regularized least squares (RLS) estimation problem (Tikhonov regularization setting) and the theory of ill-posed inverse problems is an example of how spectral regularization algorithms are related to the theory of ill-posed inverse problems.
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Andrey Nikolayevich Tikhonov (Russian: Андре́й Никола́евич Ти́хонов; 17 October 1906 – 7 October 1993) was a leading Soviet Russian mathematician and geophysicist known for important contributions to topology, functional analysis, mathematical physics, and ill-posed problems.
For instance, Tikhonov regularization corresponds to a normally distributed prior on that is centered at 0. To see this, first note that the OLS objective is proportional to the log-likelihood function when each sampled y i {\displaystyle y^{i}} is normally distributed around w T ⋅ x i {\displaystyle w^{\mathsf {T}}\cdot x^{i}} .