KL.divergence(X, Y, k = 10, algorithm=c("kd_tree", "cover_tree", "brute"))
KLx.divergence(X, Y, k = 10, algorithm="kd_tree")
Arguments
X
An input data matrix.
Y
An input data matrix.
k
The maximum number of nearest neighbors to search. The default value
is set to 10.
algorithm
nearest neighbor search algorithm.
Value
Return the Kullback-Leibler divergence from X to Y.
Details
If p(x) and q(x) are two continuous probability density functions,
then the Kullback-Leibler divergence of q from p is defined as
\(E_p[\log \frac{p(x)}{q(x)}]\).
KL.* versions return divergences from C code to R but KLx.* do not.
References
S. Boltz, E. Debreuve and M. Barlaud (2007).
“kNN-based high-dimensional Kullback-Leibler distance for tracking”.
Image Analysis for Multimedia Interactive Services, 2007. WIAMIS '07. Eighth International Workshop on.
S. Boltz, E. Debreuve and M. Barlaud (2009).
“High-dimensional statistical measure for region-of-interest tracking”.
Trans. Img. Proc., 18:6, 1266--1283.