Research map / Learning theory and algorithms / Matrix and tensor methods
Tensor denoising and completion based on ordinal observations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Ordinal-T rather than Continuous-T or 1bit-sign-T | Ordinal tensor prediction on HCP · HCP | MAD: Ordinal-T: 0.1607 (0.0005); Continuous-T: 0.2530 (0.0002); 1bit-sign-T: 0.3566 (0.0010) | Helped |
| Use Ordinal-T rather than Continuous-T or 1bit-sign-T | Ordinal tensor prediction on HCP · HCP | MCR: Ordinal-T: 0.1606 (0.0005); Continuous-T: 0.1599 (0.0002); 1bit-sign-T: 0.1563 (0.0010) | Mixed |
| Use Ordinal-T rather than Continuous-T or 1bit-sign-T | Ordinal tensor completion on InCarMusic · InCarMusic | MAD: Ordinal-T: 1.37 (0.039); Continuous-T: 2.39 (0.152); 1bit-sign-T: 1.39 (0.003) | Helped |
| Use Ordinal-T rather than Continuous-T or 1bit-sign-T | Ordinal tensor completion on InCarMusic · InCarMusic | MCR: Ordinal-T: 0.59 (0.009); Continuous-T: 0.94 (0.027); 1bit-sign-T: 0.81 (0.005) | Helped |
| Exploit the full multilinear tensor structure instead of unfolding into a matrix | Ordinal-T versus Ordinal-M · Simulated ordinal tensors | prediction error: ~ 30% reduction in error | Helped |
| Use multiple ordered levels rather than binary observations | Ordinal-T · Simulated ordinal tensors | estimation error | Helped |
| Increase the signal level from moderate values toward either extreme | Ordinal-T · Complete simulated tensors | relative MSE | Hurt |
Closest papers by meaning
- Beyond the Signs: Nonparametric Tensor Completion via Sign Series (NeurIPS 2021 · similarity 0.89)
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- Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model (ICLR 2024 · similarity 0.88)
- Score-Based Model for Low-Rank Tensor Recovery (AAAI 2026 · similarity 0.87)
- Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.86)
- Low-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression (ICML 2025 · similarity 0.86)
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