Research map / Learning theory and algorithms / Matrix and tensor methods
Hyperplane Clustering via Dual Principal Component Pursuit
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sequential DPCP with distance-based point weighting instead of hard thresholding | Sequential hyperplane clustering · Corrupted synthetic data | clustering performance | Helped |
| Use DPCP instead of SVD to compute each cluster's normal vector | K-Hyperplanes · Real 3D point clouds | clustering performance | No clear effect |
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