Research map / Optimization / Nonconvex and smooth optimization
Bilinear Parameterization for Non-Separable Singular Value Penalties
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the bilinear formulation instead of ADMM with weighted nuclear norm regularization | Structure-from-motion matrix recovery · Cathedral, Door, and École | log-loss: Cathedral: -2.4548 -> -3.8110; Door: -1.6247 -> -4.6275; École: -1.6869 -> -4.4264 | Helped |
| Use the bilinear formulation instead of ADMM with the best reported LI or SV weights | Structure-from-motion matrix recovery · Back, Heart, and Paper | log-loss: Back: -5.2560 -> -5.2863; Heart: -5.0829 -> -5.1153; Paper: -5.8509 -> -5.9023 | Helped |
| Replace ADMM with the bilinear method | Non-rigid structure from motion · Articulated, Balloon, Paper, Stretch, and Tearing | log-loss: Articulated: -2.221 -> -2.415; Balloon: -2.529 -> -2.657; Paper: -2.338 -> -2.560; Stretch: -2.395 -> -2.622; Tearing: - | Helped |
| Replace ADMM with the bilinear method | Non-rigid structure from motion · Balloon, Paper, Stretch, and Tearing | reconstruction error (mm): Balloon: 9.29 -> 8.29; Paper: 6.95 -> 6.70; Stretch: 7.83 -> 7.66; Tearing: 29.90 -> 11.26 | Helped |
| Replace ADMM with the bilinear method | Non-rigid structure from motion · Articulated | reconstruction error (mm): 14.55 -> 16.10 | Hurt |
Closest papers by meaning
- A Unified Optimization Framework for Low-Rank Inducing Penalties (CVPR 2020 · similarity 0.88)
- Revisiting the Variable Projection Method for Separable Nonlinear Least Squares Problems (CVPR 2017 · similarity 0.86)
- Cogradient Descent for Bilinear Optimization (CVPR 2020 · similarity 0.85)
- Minimizing the Maximal Rank (CVPR 2016 · similarity 0.84)
- Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints (ICML 2024 · similarity 0.84)
- Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems (AAAI 2024 · similarity 0.83)
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