Research map / Learning theory and algorithms / Matrix and tensor methods
A Pseudo-Bayesian Algorithm for Robust PCA
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace PCP preprocessing with PB-RPCA before SSC (motion segmentation with outliers) | SSC on Hopkins155 trajectories · Hopkins155, outlier ratio 0.1-0.4, with and without sub-sampling | segmentation error % (mean) | Helped |
| Adopt symmetric PB-RPCA objective (Psi_c and Psi_r both learned) instead of asymmetric version (ablation) | PB-RPCA phase transition recovery · Simulated 200x200 phase transition | recovery success region | Helped |
| Use PB-RPCA's non-separable, concave log-det objective instead of PCP's convex penalty | Rank-one cube-structured low-rank data with sparse outliers (hard case) · Synthetic hard case, increasing outlier ratio | success rate | Helped |
| Use PB-RPCA instead of PCP for recovery on Gaussian iid low-rank phase transition data | RPCA phase transition · 200x200 simulated, Gaussian iid low-rank, outlier ratio vs rank ratio | recovery success region | Helped |
| Replace log-det terms with trace terms (reduces to convex PCP) | PB-RPCA objective · Theoretical analysis | objective equivalence | No clear effect |
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