Research map / Learning theory and algorithms / Matrix and tensor methods
Beyond CCA: Moment Matching for Multi-View Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace order-3 T-cumulant tensors with generalized S-covariance matrices (second-order information only) as targets for joint diagonalization | DCCA / DCCAg estimation · Synthetic discrete data (2D and 20D) | normalized l1 error | No clear effect |
| Use DCCA (DCCA-style discrete CCA with NOJD) instead of DICA or NMF when the problem is over-complete | DCCA · Synthetic discrete data, over-complete case (Fig. 2 left) | normalized l1 error | Helped |
| Use NMF with random initialization instead of structured initialization in high dimensions | NMF baseline · Synthetic discrete data, high-dimensional (20D) | normalized l1 error | Hurt |
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