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Learning of tree-structured Gaussian graphical models on distributed data under communication constraints

Tavassolipour, M ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/TSP.2018.2876325
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our simulation results on both synthetic and real-world datasets show that our strategies achieve a desired accuracy in inferring the underlying structure while spending a small budget on communication. © 1991-2012 IEEE
  6. Keywords:
  7. Chow-Liu algorithm ; Gaussian graphical model ; Structure learning ; Budget control ; Covariance matrix ; Data structures ; Forestry ; Gaussian distribution ; Graphic methods ; Signal processing ; Speech recognition ; Computational model ; Covariance matrices ; Distributed database ; Gaussian graphical models ; GraphicaL model ; Signal processing algorithms ; Structure-learning ; Trees (mathematics)
  8. Source: IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN)
  9. URL: https://signalprocessingsociety.org/publications-resources/ieee-transactions-signal-processing/learning-tree-structured-gaussian