phi.gaussian {DPpack} | R Documentation |
Transform Function for Gaussian Kernel Approximation
Description
This function maps an input data row x with a given prefilter to an output value in such a way as to approximate the Gaussian kernel (Chaudhuri et al. 2011).
Usage
phi.gaussian(x, theta)
Arguments
x |
Vector or matrix corresponding to one row of the dataset X. |
theta |
Randomly sampled prefilter vector of length n+1, where n is the length of x. |
Value
Mapped value corresponding to one element of the transformed space.
References
Chaudhuri K, Monteleoni C, Sarwate AD (2011). “Differentially Private Empirical Risk Minimization.” Journal of Machine Learning Research, 12(29), 1069-1109. https://jmlr.org/papers/v12/chaudhuri11a.html.
Examples
x <- c(1,2,3)
theta <- c(0.1, 1.1, -0.8, 3)
phi.gaussian(x, theta)
[Package DPpack version 0.2.0 Index]