Research map / Learning theory and algorithms / Matrix and tensor methods
Tensor Decomposition via Joint Matrix Schur Decomposition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Gauss-Newton joint Schur optimizer instead of Jacobi (Haardt & Nossek) for approximate joint triangularization | joint matrix Schur decomposition (d=10, 100 nearly commuting matrices) · simultaneous triangularization synthetic | runtime to comparable objective value | Helped |
| Gauss-Newton instead of Jacobi optimizer | joint matrix Schur decomposition · simultaneous triangularization synthetic | final objective value | No clear effect |
| Increasing number of random projections in the modified algorithm (ablation) | proposed method with random projections · synthetic symmetric nonorthogonal tensors | recovery error | No clear effect |
| Gauss-Newton joint Schur decomposition instead of cpd3-sgsd and no-tenfact on crowdsourcing label prediction | tensor decomposition for crowdsourcing label prediction · birds and dogs crowdsourcing datasets | score | No clear effect |
| Gauss-Newton on synthetic symmetric nonorthogonal tensor decomposition vs cpd3-sgsd | tensor decomposition · synthetic d=10,20 symmetric nonorthogonal tensors | recovery error | No clear effect |
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