learn_smooth_approx_graph: Learns a smooth approximated graph from an observed data...
In spectralGraphTopology: Learning Graphs from Data via Spectral Constraints
View source: R/constrLaplacianRank.R
learn_smooth_approx_graph R Documentation
Learns a smooth approximated graph from an observed data matrix.
Check out https://mirca.github.io/spectralGraphTopology for code examples.
Description
Learns a smooth approximated graph from an observed data matrix.
Check out https://mirca.github.io/spectralGraphTopology for code examples.
Usage
learn_smooth_approx_graph(Y, m)
Arguments
Y
a p-by-n data matrix, where p is the number of nodes and n is the number of
features (or data points per node)
m
the maximum number of possible connections for a given node used
to build an affinity matrix
Value
A list containing the following elements:
laplacian
the estimated Laplacian Matrix
Author(s)
Ze Vinicius and Daniel Palomar
References
Nie, Feiping and Wang, Xiaoqian and Jordan, Michael I. and Huang, Heng.
The Constrained Laplacian Rank Algorithm for Graph-based Clustering, 2016,
AAAI'16. http://dl.acm.org/citation.cfm?id=3016100.3016174
spectralGraphTopology documentation built on March 18, 2022, 7:35 p.m.
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View source: R/constrLaplacianRank.R
| learn_smooth_approx_graph | R Documentation |
Learns a smooth approximated graph from an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.
Description
Learns a smooth approximated graph from an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.
Usage
learn_smooth_approx_graph(Y, m)
Arguments
Y |
a p-by-n data matrix, where p is the number of nodes and n is the number of features (or data points per node) |
m |
the maximum number of possible connections for a given node used to build an affinity matrix |
Value
A list containing the following elements:
|
the estimated Laplacian Matrix |
Author(s)
Ze Vinicius and Daniel Palomar
References
Nie, Feiping and Wang, Xiaoqian and Jordan, Michael I. and Huang, Heng. The Constrained Laplacian Rank Algorithm for Graph-based Clustering, 2016, AAAI'16. http://dl.acm.org/citation.cfm?id=3016100.3016174
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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