DNNLasso: Scalable Graph Learning for Matrix-Variate Data
<|reference_start|>DNNLasso: Scalable Graph Learning for Matrix-Variate Data: We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices. Due to the complicated structure of Kronecker-product precision matrices in the commonly used matrix-variate Gaussian graphical models, a sparser Kronecker-sum structure was proposed recently based on the Cartesian product of graphs. However, existing methods for estimating Kronecker-sum structured precision matrices do not scale well to large scale datasets. In this paper, we introduce DNNLasso, a diagonally non-negative graphical lasso model for estimating the Kronecker-sum structured precision matrix, which outperforms the state-of-the-art methods by a large margin in both accuracy and computational time. Our code is available at https://github.com/YangjingZhang/DNNLasso.<|reference_end|>
@article{lin2024dnnlasso:,
    title={DNNLasso: Scalable Graph Learning for Matrix-Variate Data},
    author={Meixia Lin and Yangjing Zhang},
    journal={arXiv preprint arXiv:2403.02608},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02608},
    primaryClass={cs.LG math.OC}
}