Product graph learning from multi-domain data with sparsity and rank constraints
SK Kadambari, SP Chepuri - IEEE Transactions on Signal …, 2021 - ieeexplore.ieee.org
In this paper, we focus on learning product graphs from multi-domain data. We assume that
the product graph is formed by the Cartesian product of two smaller graphs, which we refer
to as graph factors. We pose the product graph learning problem as the problem of
estimating the graph factor Laplacian matrices. To capture local interactions in data, we seek
sparse graph factors and assume a smoothness model for data. We propose an efficient
iterative solver for learning sparse product graphs from data. We then extend this solver to …
the product graph is formed by the Cartesian product of two smaller graphs, which we refer
to as graph factors. We pose the product graph learning problem as the problem of
estimating the graph factor Laplacian matrices. To capture local interactions in data, we seek
sparse graph factors and assume a smoothness model for data. We propose an efficient
iterative solver for learning sparse product graphs from data. We then extend this solver to …
Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints
S Kiran Kadambari, S Prabhakar Chepuri - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
In this paper, we focus on learning product graphs from multi-domain data. We assume that
the product graph is formed by the Cartesian product of two smaller graphs, which we refer
to as graph factors. We pose the product graph learning problem as the problem of
estimating the graph factor Laplacian matrices. To capture local interactions in data, we seek
sparse graph factors and assume a smoothness model for data. We propose an efficient
iterative solver for learning sparse product graphs from data. We then extend this solver to …
the product graph is formed by the Cartesian product of two smaller graphs, which we refer
to as graph factors. We pose the product graph learning problem as the problem of
estimating the graph factor Laplacian matrices. To capture local interactions in data, we seek
sparse graph factors and assume a smoothness model for data. We propose an efficient
iterative solver for learning sparse product graphs from data. We then extend this solver to …
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