kernel_gaussian

Create gaussian kernel gram matrix from distance matrix

Fast, flexible and user-friendly tools for distribution comparison
through direct density ratio estimation. The estimated density ratio can be
used for covariate shift adjustment, outlier-detection, change-point detection,
classification and evaluation of synthetic data quality. The package implements
multiple non-parametric estimation techniques (unconstrained least-squares
importance fitting, ulsif(), Kullback-Leibler importance estimation procedure,
kliep(), spectral density ratio estimation, spectral(), kernel mean matching,
kmm(), and least-squares hetero-distributional subspace search, lhss()).
with automatic tuning of hyperparameters. Helper functions are available for
two-sample testing and visualizing the density ratios. For an overview on
density ratio estimation, see Sugiyama et al. (2012) <doi:10.1017/CBO9781139035613>
for a general overview, and the help files for references on the specific
estimation techniques.

Thom Volker

densityratio

Distribution Comparison Through Density Ratio Estimation

Carlos Gonzalez Poses

Erik-Jan van Kesteren

kernel_gaussian function

<dl><dt>dist</dt>
<dd>A numeric distance matrix</dd>
<dt>sigma</dt>
<dd>A scalar with the length-scale parameter</dd></dl>

Arguments

Create gaussian kernel gram matrix from distance matrix — kernel_gaussian

<dl>

<dt>dist</dt>
<dd>A numeric distance matrix</dd>


<dt>sigma</dt>
<dd>A scalar with the length-scale parameter</dd>

</dl>

kernel_gaussian: Create gaussian kernel gram matrix from distance matrix

Description

Arguments