التفاصيل البيبلوغرافية
| العنوان: |
l0 Penalized Maximum Likelihood Estimation of Sparse Covariance Matrices |
| المؤلفون: |
Fatima, Ghania, Stoica, Peter, 1949, Babu, Prabhu |
| المصدر: |
IEEE Signal Processing Letters. 32:66-70 |
| مصطلحات موضوعية: |
Covariance matrices, Maximum likelihood estimation, Sparse matrices, Minimization, Tuning, Signal processing algorithms, Optimization, Bayes methods, Wireless communication, Taylor series, Covariance matrix estimation, EBIC, l(0) penalized maximum likelihood |
| الوصف: |
In this letter we present a framework for estimating sparse covariance matrices, wherein we solve the l(0)-norm penalized maximum likelihood estimation problem using the extended Bayesian information criterion (EBIC), a high dimensional model selection rule. The framework combines choosing the sparsity pattern and estimating the covariance matrix in a single step, eliminating the need for any hyper-parameter tuning. Using the framework we propose a cyclic majorization-minimization based technique and apply it to synthetic data to evaluate its performance in terms of normalized root mean square error (NRMSE) and Kullback Leibler (KL) divergence. |
| وصف الملف: |
print |
| URL الوصول: |
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-546837 |
| قاعدة البيانات: |
SwePub |