Research map / Graph learning / Graph algorithms and clustering
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase l1 regularization lambda from 0 to 10 (ablation) | l1-norm Laplacian constrained graph learning · Barabasi-Albert p=50 | relative error: 0.14 -> 0.99 | Hurt |
| increase l1 regularization lambda (ablation) | l1-norm Laplacian constrained graph learning · Barabasi-Albert p=50 | number of nonzero edges: 135 -> 1225 | Hurt |
| replace l1 penalty with nonconvex MCP or SCAD penalty | Laplacian constrained GGM estimation · Barabasi-Albert p=50, n/p=100 | F-score: F-score 1 at lambda 0.1 or 0.25 | Helped |
| replace l1 penalty with nonconvex MCP/SCAD | Laplacian constrained GGM estimation · Barabasi-Albert, varying n/p | relative error and F-score | Helped |
| replace GLE-ADMM with NGL-SCAD/NGL-MCP | graph learning · 2019-nCoV data | computation time (s): 2.9 -> 0.7 | Helped |
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