learn_smooth_approx_graph {spectralGraphTopology}

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