Corpus ID: 211010853

The Sylvester Graphical Lasso (SyGlasso)

@article{Wang2020TheSG,
  title={The Sylvester Graphical Lasso (SyGlasso)},
  author={Yu Wang and Byoungwook Jang and Alfred O. Hero},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.00288}
}
  • Yu Wang, Byoungwook Jang, Alfred O. Hero
  • Published 2020
  • Mathematics, Computer Science
  • ArXiv
  • This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso (Greenewald et al., 2019) that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 29 REFERENCES

    Partial Correlation Estimation by Joint Sparse Regression Models.

    VIEW 11 EXCERPTS
    HIGHLY INFLUENTIAL

    Motivation, emotion, and their inhibitory control mirrored in brain oscillations

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Patterns of regional brain activity in alcohol-dependent subjects.

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Krylov Subspace Methods for Linear Systems with Tensor Product Structure

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL