Research map / Learning theory and algorithms / Matrix and tensor methods
Low-Rank Regression with Tensor Responses
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use HOLRR instead of other tensor regression baselines | HOLRR · synthetic linear data | RMSE | Helped |
| use HOLRR instead of LRR | HOLRR · synthetic nonlinear data | RMSE | Helped |
| enforce low multilinear rank with small rank parameter when truth is not low rank | HOLRR · illustrative image tensor | fit | Hurt |
| use RBF kernel K-HOLRR instead of linear HOLRR | HOLRR · CCDS | test RMSE: 0.8096 -> 0.7913 | Helped |
| use RBF kernel instead of polynomial kernel | K-HOLRR · CCDS | test RMSE: 0.8275 -> 0.7913 | Helped |
| use RBF kernel K-HOLRR instead of linear HOLRR | HOLRR · Meteo-UK | test RMSE: 0.5971 -> 0.5886 | Helped |
| use HOLRR instead of Greedy | HOLRR · Foursquare | test RMSE: 0.1223 -> 0.1227 | No clear effect |
| use kernelized K-HOLRR instead of Greedy | K-HOLRR · CCDS | running time: 75.47s -> 0.61s | Helped |
| use K-HOLRR instead of ADMM | K-HOLRR · Meteo-UK | running time: 40.23s -> 1.66s | Helped |
Closest papers by meaning
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- Low-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression (ICML 2025 · similarity 0.84)
- Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts (NeurIPS 2024 · similarity 0.84)
- Neural Networks Perform Sufficient Dimension Reduction (AAAI 2025 · similarity 0.83)
- OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems (AAAI 2025 · similarity 0.83)
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