KNN Cross Entropy Estimators.
crossentropy(X, Y, k=10, algorithm=c("kd_tree", "cover_tree", "brute"))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.
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.