Generalized Laplacian precision matrix estimation for graph signal processing

@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}
}
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

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