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Common Substructure Learning of Multiple Graphical Gaussian Models
Published on Oct 03, 20112855 Views
Learning underlying mechanisms of data generation is of great interest in the scientific and engineering fields amongst others. Finding dependency structures among variables in the data is one possibl
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Chapter list
Common Substructure Learning of Multiple Graphical Gaussian Models00:00
Dynamics of Graphical Model00:12
Goal of the Research01:14
Contents: GGM & Common Substructure Learning01:39
Background: Graphical Gaussian Model GGM）01:44
Related Work: Structure Learning of GGM02:45
Our Proposal: Common Substructure of GGMs04:07
Our Proposal: Problem Formulation05:08
Our Proposal: Relation to The Existing Work05:51
Contents: Algorithm06:54
Block Coordinate Descent07:03
Optimization of Diagonal Entries08:10
Optimization of Non-diagonal Entries08:56
Solution to Each Case10:16
Contents:Simulation11:23
Simulation Setup11:30
Baseline Methods12:03
Result12:35
Contents: Application to Anomaly Detection13:38
Application to Anomaly Detection13:46
Simulation Setting15:11
Result (Detection Performance)15:41
Result (Anomaly Score)16:05
Summary & Conclusion17:06