FNN: crossentropy – R documentation

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FNN

crossentropy

Cross Entropy

Description

KNN Cross Entropy Estimators.

Usage

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: lishengqiao@yahoo.com.

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.

FNN

Fast Nearest Neighbor Search Algorithms and Applications

v1.1.3

GPL (>= 2)

Authors

Alina Beygelzimer, Sham Kakadet and John Langford (cover tree library), Sunil Arya and David Mount (ANN library 1.1.2 for the kd-tree approach), Shengqiao Li

Initial release

2019-02-15