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DTSTAMP:20190719T085743Z
LOCATION:HG F 1
DTSTART;TZID=Europe/Stockholm:20190612T143000
DTEND;TZID=Europe/Stockholm:20190612T150000
UID:submissions.pasc-conference.org_PASC19_sess138_msa168@linklings.com
SUMMARY:Learning Large-Scale Sparse Graphical Models: Theory, Algorithm, a
 nd Applications
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Chemi
 stry and Materials, Physics, Engineering\n\nLearning Large-Scale Sparse Gr
 aphical Models: Theory, Algorithm, and Applications\n\nSojoudi\n\nLearning
  models from data has a significant impact on many disciplines, including 
 computer vision, medical imaging, social networks and signal processing. I
 n the network inference problem, one may model the relationships between t
 he network components through an underlying inverse covariance matrix. The
  sparse inverse covariance estimation problem is commonly solved using an 
 l1-regularized Gaussian maximum likelihood estimator, known as “grap
 hical lasso”. Despite the popularity of graphical lasso, its computa
 tional cost becomes prohibitive for large data sets. In this talk, we will
  develop new notions of sign-consistent matrices and inverse-consistent ma
 trices to obtain key properties of graphical lasso and prove that although
  the complexity of solving graphical lasso is high, the sparsity pattern o
 f its solution has a simple formula if a sparse graphical model is sought.
  We will prove – under mild assumptions – that the graphical l
 asso estimator can be retrieved by soft-thresholding the sample covariance
  matrix and solving a maximum determinant matrix completion (MDMC) problem
 , and describe a Newton-CG algorithm to efficiently solve the MDMC problem
 . We will illustrate our results in different case studies.
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