Research map / Learning theory and algorithms / Clustering
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use dense thresholded graphs with p = O(1) | TIP-SC · Two-subspace UoS | clustering error rate | Helped |
| Allow sampling below the subspace dimension (ρ < 1) | TIP-SC · Two-subspace UoS | clustering error rate | Helped |
| Allow subspaces with substantial overlap | TIP-SC · Two-subspace UoS | clustering error rate | Helped |
| Add independent Gaussian noise with σ < σ* | TIP-SC · Noisy two-subspace UoS | clustering error rate | Hurt |
| Use larger p and q instead of setting q = 0 | TIP-SC · Synthetic UoS; connection-rate experiments | clustering error rate | Helped |
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