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 Matrixcovariance()Gets the estimated covariance matrix.MatrixinverseCovariance()Gets the inverse of the estimated covariance matrix.booleanisConverged()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:
covariancein interfaceCovarianceSelectionSolver- Returns:
- the estimated covariance matrix
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inverseCovariance
public Matrix inverseCovariance()
Gets the inverse of the estimated covariance matrix.- Specified by:
inverseCovariancein interfaceCovarianceSelectionSolver- Returns:
- the inverse of the estimated covariance matrix
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isConverged
public boolean isConverged()
Checks if the algorithm converges.- Returns:
trueif the algorithm stops before maximum iteration is reached
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