Learning Graphs from Data via Spectral Constraints
Computes the Adjacency linear operator which maps a vector of weights ...
Computes the accuracy between two matrices
Computes the Astar operator.
Constructs a block diagonal matrix from a list of square matrices
Cluster a k-component graph from data using the Constrained Laplacian ...
Computes the degree operator from the vector of edge weights.
Computes the Dstar operator, i.e., the adjoint of the D operator.
Computes the false discovery rate between two matrices
Computes the fscore between two matrices
Computes the Laplacian linear operator which maps a vector of weights ...
Learn a bipartite graphLearns a bipartite graph on the basis of an obs...
Learns a bipartite k-component graphJointly learns the Laplacian and A...
Learn the Combinatorial Graph Laplacian from dataLearns a graph Laplac...
Learn graphs from a smooth signal representation approachThis function...
Learn the Laplacian matrix of a k-component graphLearns a k-component ...
Learn the weighted Laplacian matrix of a graph using the ADMM method
Learn the weighted Laplacian matrix of a graph using the MM method
Learns a smooth approximated graph from an observed data matrix. Check...
Learn a graph from smooth signalsThis function learns a connected grap...
Computes the Lstar operator.
Computes the negative predictive value between two matrices
Computes the recall between two matrices
Computes the relative error between the true and estimated matrices
Computes the specificity between two matrices
Package spectralGraphTopology
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.
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