Compute Kullback-Leibler symmetric distance.
KL.dist(X, Y, k = 10, algorithm=c("kd_tree", "cover_tree", "brute"))
KLx.dist(X, Y, k = 10, algorithm="kd_tree")An input data matrix.
An input data matrix.
The maximum number of nearest neighbors to search. The default value is set to 10.
nearest neighbor search algorithm.
Return the Kullback-Leibler distance between X and Y.
Kullback-Leibler distance is the sum of divergence q(x) from p(x) and p(x) from q(x) .
KL.* versions return distances from C code to R but KLx.* do not.
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.
# NOT RUN {
set.seed(1000)
X<- rexp(10000, rate=0.2)
Y<- rexp(10000, rate=0.4)
KL.dist(X, Y, k=5)
KLx.dist(X, Y, k=5)
#thoretical distance = (0.2-0.4)^2/(0.2*0.4) = 0.5
# }