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SAS® Visual Data Mining and Machine Learning: Procedures
2020.1
|PDF 
フィードバック
 
  • Introduction
  • Shared Concepts
  • The ASTORE Procedure
  • The BNET Procedure
  • The BOOLRULE Procedure
  • The FACTMAC Procedure
  • The FASTKNN Procedure
  • The FISM Procedure
  • The FOREST Procedure
  • The GMM Procedure
  • The GRADBOOST Procedure
  • The GVARCLUS Procedure
    • Overview
    • Getting Started
    • Syntax
    • Details
      • Missing Values
      • Graphical Lasso
      • Graphical Representation of Precision Matrix
      • Preprocessing
      • Selection
      • Displayed Output
      • ODS Table Names
    • Examples
    • References
  • The KPCA Procedure
  • The MBANALYSIS Procedure
  • The MTLEARN Procedure
  • The MWPCA Procedure
  • The NNET Procedure
  • The OPTBINNING Procedure
  • The RPCA Procedure
  • The SEMISUPLEARN Procedure
  • The SPARSEML Procedure
  • The SVDD Procedure
  • The SVMACHINE Procedure
  • The TEXTMINE Procedure
  • The TMSCORE Procedure
  • The TSNE Procedure
 

The GVARCLUS Procedure

Overview
Getting Started
Syntax
Details
Examples
References

Selection

To select the optimal lamda in the penalized log likelihood of the graphical lasso, PROC GVARCLUS minimizes the following fit statistics over all the steps,

minus log det ModifyingAbove normal upper Theta With caret plus normal upper T normal r normal a normal c normal e left-parenthesis upper S ModifyingAbove normal upper Theta With caret right-parenthesis plus sigma-summation Underscript 1 less-than-or-equal-to i less-than j less-than-or-equal-to p Endscripts upper I left-parenthesis ModifyingAbove theta With caret Subscript i j Baseline not-equals 0 right-parenthesis

where ModifyingAbove normal upper Theta With caret equals left-parenthesis ModifyingAbove theta With caret Subscript i j Baseline right-parenthesis Subscript 1 less-than-or-equal-to i comma j less-than-or-equal-to p denotes the estimates that are obtained at each step.

Copyright © SAS Institute Inc. All rights reserved.
Last updated: November 11, 2020
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Copyright © SAS Institute Inc. All Rights Reserved