Supplementary PDF.pdf

wang18f-supp.pdf
Preview of Supplementary PDF
🔗 Source: proceedings.mlr.press
📊 Size: 1.21 MB
👤 Author: Beilun Wang, Arshdeep Sekhon, Yanjun Qi
⬇️ Downloads: 32

Summary

The authors propose a fast and scalable joint estimator, JEEK, for integrating additional knowledge in learning multiple related sparse Gaussian graphical models. JEEK can be easily paralleled and is group entry-wise and parallelizing optimizable. The estimator can be optimized asynchronously in a group entry-wise manner, and its performance is compared to other methods on simulated and real-world datasets, showing improved accuracy and efficiency. The approach has a close connection to hierarchical Bayesian models, where the additional knowledge weight matrices are parameters of the prior distribution of the precision matrices.

Description

The authors propose a fast and scalable joint estimator, JEEK, for integrating additional knowledge in learning multiple related sparse Gaussian graphical...

Technical Information

  • File Format: PDF
  • File Size: 1.21 MB
  • Pages: 18
  • Language: EN
  • Author: Beilun Wang, Arshdeep Sekhon, Yanjun Qi
  • Total Downloads: 32
  • Last Updated: 1 week ago

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