Covariance Selection

Revision for “Covariance Selection” created on April 26, 2015 @ 13:43:05

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Covariance Selection
<p>The covariance selection problem is formulated as this:</p>
<p style="text-align: center">\max_{X} \log(\det X) - Tr(\Sigma X)-\rho Card(X)</p>
<p>in the variable of X in S^n, where \Sigma \in S^n is the sample covariance matrix, Card(X) the cardinality of X, i.e., the number of non-zero coefficients in X. \rho > 0 is a parameter controlling the tradeoff between the likelihood and structure.</p>
<h1>References</h1>
<ol>
<li>"O. Banerjee, L. E. Ghaoui and A. d’Aspremont, "Model Selection Through Sparse Maximum Likelihood Estimation for multivariate Gaussian or Binary Data," Journal of Machine Learning Research, 9, pp. 485-516, March 2008."</li>
<li>"A. d’Aspremont, "Identifying Small Mean Reverting Portfolios", 2008."</li>
</ol>
<p>&nbsp;</p>



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