Research map / Learning theory and algorithms / Matrix and tensor methods
A Nullspace Property for Subspace-Preserving Recovery
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Normalize data columns with l2-norm instead of other lp-norms (p != 2) | subspace-preserving recovery (SNSP verification) · theoretical analysis, rotation-invariance example | SNSP satisfaction under orthogonal transformation | Helped |
| Normalize columns with l-infinity norm instead of l2 | subspace-preserving recovery (SNSP) · Example 1 (three subspaces in R^3) | SNSP satisfaction | Hurt |
| Replace the data-matrix verification of SNSP with verification on only the extreme points of the symmetrized convex hull of each subspace's points | SNSP verification · theoretical analysis (Lemma 1) | equivalence of SNSP verification | No clear effect |
| Sufficient condition based on inner-radius vs outer-radius comparison (Cor. 2) instead of incoherence condition (You & Vidal 2015) | subspace-preserving recovery guarantee · theoretical comparison with You & Vidal (2015) | strength of sufficient condition | Helped |
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