Cross Entropy

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Description

KNN Cross Entropy Estimators.

Usage

1
  crossentropy(X, Y, k=10, algorithm=c("kd_tree", "cover_tree", "brute"))

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.

Details

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)].

Value

a vector of length k for crossentropy estimates using 1:k nearest neighbors, respectively.

Author(s)

Shengqiao Li. To report any bugs or suggestions please email: shli@stat.wvu.edu.

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.