The covariance selection problem is formulated as this:
in the variable of in , where is the sample covariance matrix, the cardinality of , i.e., the number of non-zero coefficients in . is a parameter controlling the tradeoff between the likelihood and structure.
- “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.”
- “A. d’Aspremont, “Identifying Small Mean Reverting Portfolios”, 2008.”