Package 'spectralGraphTopology'
| Title: |
Learning Graphs from Data via Spectral Constraints |
| Description: |
In the era of big data and hyperconnectivity, learning
high-dimensional structures such as graphs from data has become
a prominent task in machine learning and has found applications
in many fields such as finance, health care, and networks.
'spectralGraphTopology' is an open source, documented, and
well-tested R package for learning graphs from data. It
provides implementations of state of the art algorithms such as
Combinatorial Graph Laplacian Learning (CGL), Spectral Graph
Learning (SGL), Graph Estimation based on
Majorization-Minimization (GLE-MM), and Graph Estimation based
on Alternating Direction Method of Multipliers (GLE-ADMM). In
addition, graph learning has been widely employed for
clustering, where specific algorithms are available in the
literature. To this end, we provide an implementation of the
Constrained Laplacian Rank (CLR) algorithm. |
| Authors: |
Ze Vinicius [cre, aut],
Daniel P. Palomar [aut] |
| Maintainer: |
Ze Vinicius <[email protected]> |
| License: |
GPL-3 |
| Version: |
0.2.3 |
| Built: |
2024-11-11 04:31:05 UTC |
| Source: |
https://github.com/dppalomar/spectralgraphtopology |
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