# KL.dist {FNN}

### Description

Compute Kullback-Leibler symmetric distance.

### Usage

KL.dist(X, Y, k = 10, algorithm=c("kd_tree", "cover_tree", "brute")) KLx.dist(X, Y, k = 10, algorithm="kd_tree")

### 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

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.

### Values

Return the Kullback-Leibler distance between `X`

and `Y`

.

### 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*.

S. Boltz, E. Debreuve and M. Barlaud (2009). “High-dimensional statistical measure for region-of-interest tracking”. *Trans. Img. Proc.*, **18**:6, 1266--1283.

### See Also

`KL.divergence`

.

### Examples

Documentation reproduced from package FNN, version 1.1. License: GPL (>= 2.1)