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Accession Number ADA581712
Title Kronecker Graphical Lasso.
Publication Date Aug 2012
Media Count 5p
Personal Author I. A. Hero S. Zhou T. Tsiligkaridis
Abstract We consider high-dimensional estimation of a (possibly sparse) Kronecker-decomposable covariance matrix given i.i.d. Gaussian samples. We propose a sparse covariance estimation algorithm, the Kronecker Graphical Lasso (KGlasso), for the high-dimensional setting that takes advantage of structure and sparsity. Convergence and limit point characterization of this iterative algorithm are established. Compared to standard Glasso, KGlasso has low computational complexity as the dimension of the covariance matrix increases. We derive a tight mean squared error (MSE) convergence rate for KGlasso and show that it outperforms standard Glasso and the flip-flop algorithm. Simulations validate these results and show that KGlasso outperforms the maximum-likelihood solution (FF) in the high-dimensional small-sample regime.
Keywords Algorithms
Computerized simulation
Covariance matrix
Flip-flop algorithm
High-dimensional consistency
Kronecker graphical lasso
L1-penalized maximum likelihood estimators
Learning machines
Maximum likelihood estimation
Monte carlo method
Multivariate gaussian models
Signal processing
Sparse covariance estimation algorithms
Standard graphical lasso algorithm
Structured covariance estimation

Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
62 - Computers, Control & Information Theory
Corporate Author Michigan Univ., Ann Arbor. Dept. of Electrical Engineering and Computer Science.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1325
Contract Number W911NF-11-1-0391

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