Class CovarianceSelectionGLASSOFAST
- java.lang.Object
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- dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
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- All Implemented Interfaces:
CovarianceSelectionSolver
public class CovarianceSelectionGLASSOFAST extends Object implements CovarianceSelectionSolver
GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection problem. The covariance selection problem is formulated as this: \[ \max_{X} \log(\det X) - Tr(\Sigma X)-\rho Card(X) \] 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.- See Also:
- "Sustik, M.A. and Calderhead, B., "GLASSOFAST: An efficient GLASSO implementation," UTCS Technical Report TR-12-29, November 6, 2012."
- "A. d'Aspremont, "Identifying Small Mean Reverting Portfolios", 2008."
- "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."
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Constructor Summary
Constructors Constructor Description CovarianceSelectionGLASSOFAST(CovarianceSelectionProblem problem)
Solves the maximum likelihood problem for covariance selection.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description Matrix
covariance()
Gets the estimated covariance matrix.Matrix
inverseCovariance()
Gets the inverse of the estimated covariance matrix.boolean
isConverged()
Checks if the algorithm converges.
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Constructor Detail
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CovarianceSelectionGLASSOFAST
public CovarianceSelectionGLASSOFAST(CovarianceSelectionProblem problem)
Solves the maximum likelihood problem for covariance selection.- Parameters:
problem
- the covariance selection problem
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Method Detail
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covariance
public Matrix covariance()
Gets the estimated covariance matrix.- Specified by:
covariance
in interfaceCovarianceSelectionSolver
- Returns:
- the estimated covariance matrix
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inverseCovariance
public Matrix inverseCovariance()
Gets the inverse of the estimated covariance matrix.- Specified by:
inverseCovariance
in interfaceCovarianceSelectionSolver
- Returns:
- the inverse of the estimated covariance matrix
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isConverged
public boolean isConverged()
Checks if the algorithm converges.- Returns:
true
if the algorithm stops before maximum iteration is reached
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