sigma_finder: sigma_finder: heuristic to find sigma for the Ng kernel
In Spectrum: Fast Adaptive Spectral Clustering for Single and Multi-View Data
Description
Usage
Arguments
Value
Examples
View source: R/kernel_functions.R
Description
This is a heuristic to find the sigma for the kernel from the Ng spectral clustering algorithm.
It returns a global sigma. It uses the mean K nearest neighbour distances of all samples to
determine sigma.
Usage
1
sigma_finder(mat, NN = 3)
Arguments
mat
Data frame or matrix: with points as columns, features as rows
NN
Numerical value: the number of nearest neighbours to use (default=3)
Value
A global sigma
Examples
1
sig <- sigma_finder(blobs)
Spectrum documentation built on Feb. 10, 2020, 9:07 a.m.
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Description Usage Arguments Value Examples
View source: R/kernel_functions.R
Description
This is a heuristic to find the sigma for the kernel from the Ng spectral clustering algorithm. It returns a global sigma. It uses the mean K nearest neighbour distances of all samples to determine sigma.
Usage
1 | sigma_finder(mat, NN = 3)
|
Arguments
mat |
Data frame or matrix: with points as columns, features as rows |
NN |
Numerical value: the number of nearest neighbours to use (default=3) |
Value
A global sigma
Examples
1 | sig <- sigma_finder(blobs)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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