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This mex-file applies Cao's method [38] to the input data set. If
the data set contains points of dimension `D`, it computes E
and E* for a data set of dimension 1 (taken from the first column
of the input data set), then for a data set of dimension 2 (taken
from the first two columns) up to a dimension of D. Optionally, this algorithm
extends Cao's method in a straightforward manner to use more than one
nearest neighbors.

**Syntax:**

`[E, E*] = cao(pointset, query_indices, k)`

**Input arguments:**

`pointset`- a`N`by`D`double matrix containing the coordinates of the point set, organized as`N`points of dimension`D``query_indices`- query points are taken out of the pointset, query_indices is a vector of length`R`which contains the indices of the query points (indices may vary from 1 to N)`k`- number of nearest neighbors to compute. Cao's method can be extended to use more than only the first nearest neighbor (k=1).

**Output arguments:**

`E`and`E*`are vectors of size`D`. Please refer the Cao's article [38] for a precise description of their meaning.

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