https://pypi.org/project/structuredgraphlearning/ Recent updates to the Python Package Index for StructuredGraphLearning en https://pypi.org/project/structuredgraphlearning/0.0.4/ spectralGraphTopology provides estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverage spectral properties of the graphical models as a prior information which turn out to play key roles in unsupervised machine learning tasks such as clustering. aditya510@gmail.com Mon, 12 Apr 2021 10:00:13 GMT https://pypi.org/project/structuredgraphlearning/0.0.3/ spectralGraphTopology provides estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverage spectral properties of the graphical models as a prior information which turn out to play key roles in unsupervised machine learning tasks such as clustering. aditya510@gmail.com Mon, 12 Apr 2021 09:52:01 GMT https://pypi.org/project/structuredgraphlearning/0.0.2/ spectralGraphTopology provides estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverage spectral properties of the graphical models as a prior information which turn out to play key roles in unsupervised machine learning tasks such as clustering. aditya510@gmail.com Mon, 12 Apr 2021 09:35:25 GMT https://pypi.org/project/structuredgraphlearning/0.0.1/ spectralGraphTopology provides estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverage spectral properties of the graphical models as a prior information which turn out to play key roles in unsupervised machine learning tasks such as clustering. aditya510@gmail.com Sun, 11 Apr 2021 15:08:25 GMT