Robust PCA — what problem does it solve?
hardAnswer
- Standard PCA is sensitive to outliers (single bad row can rotate components).
- Robust PCA (Candès et al.): decompose X = L + S where L is low-rank (clean data) and S is sparse (outliers).
- Solved via convex relaxation: min ||L||_* + λ ||S||_1 (Principal Component Pursuit).
- Uses: video background subtraction, corrupt entry recovery, anomaly detection.
Check yourself — multiple choice
- Same as PCA
- Decompose X = L (low-rank) + S (sparse outliers) via min ||L||_* + λ||S||_1; used for background subtraction / corrupt recovery
- Random
- Not real
Robust PCA: X = L + S (low-rank + sparse); background subtraction, anomaly.
#dimensionality-reduction#pca
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