KNN Cross Entropy Estimators.

`crossentropy(X, Y, k=10, algorithm=c("kd_tree", "cover_tree", "brute"))`

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.

a vector of length `k`

for crossentropy estimates using `1:k`

nearest neighbors, respectively.

If `p(x)`

and `q(x)`

are two continuous probability density functions,
then the cross-entropy of `p`

and `q`

is defined as
\(H(p;q) = E_p[-\log q(x)]\).

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*.