Research map / Learning theory and algorithms / Matrix and tensor methods
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Cluster-EVD instead of simple EVD | PCA with sparse data-dependent corruptions · Simulated data (PCA-SDDC, n=500) | mean subspace error: 0.0908 vs 0.0911 | No clear effect |
| Cluster-EVD instead of simple EVD | PCA with sparse data-dependent corruptions · Lake video with simulated foreground | mean subspace error: 0.3626 vs 0.3821 | Helped |
| Using EVD or cluster-EVD instead of PCP / Alt-Min-RPCA when columns of P are sparse | PCA with sparse data-dependent corruptions · Simulated data (PCA-SDDC, n=500) | mean subspace error: ~0.09 vs ~1.0 | Helped |
| Cluster-EVD instead of simple EVD | PCA with sparse data-dependent corruptions · Simulated data (PCA-SDDC, n=500) | execution time: 0.0549 s vs 0.0255 s | Hurt |
| Dense P instead of sparse P | PCA-SDDC simulation · Simulated data (PCA-SDDC) | mean subspace error | No clear effect |
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