Scad-Penalized Complex Gaussian Graphical Model Selection
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)(2020)
摘要
We consider the problem of estimating the conditional independence graph (CIG) of a sparse, high-dimensional proper complex-valued Gaussian graphical model (CGGM). For CGGMs, the problem reduces to estimation of the inverse covariance matrix with more unknowns than the sample size. We consider a smoothly clipped absolute deviation (SCAD) penalty instead of the £1-penalty to regularize the problem, and analyze a SCAD-penalized log-likelihood based objective function to establish consistency and sparsistency of a local estimator of inverse covariance in a neighborhood of the true value. A numerical example is presented to illustrate the advantage of SCAD-penalty over the usual £1-penalty.
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关键词
Complex Gaussian graphical models,undirected graph,SCAD penalty,consistency,sparsistency
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