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@article{Pavez2016GeneralizedLP, title={Generalized Laplacian precision matrix estimation for graph signal processing}, author={Eduardo Pavez and Antonio Ortega}, journal={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2016}, pages={6350-6354} }

- Published in IEEE International Conference on Acoustics…2016
DOI:10.1109/ICASSP.2016.7472899

Graph signal processing models high dimensional data as functions on the vertices of a graph. This theory is constructed upon the interpretation of the eigenvectors of the Laplacian matrix as the Fourier transform for graph signals. We formulate the graph learning problem as a precision matrix estimation with generalized Laplacian constraints, and we propose a new optimization algorithm. Our formulation takes a covariance matrix as input and at each iteration updates one row/column of the… CONTINUE READING