Research map / Learning theory and algorithms / Matrix and tensor methods
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TRLRF instead of TRWOPT | HSI completion, 3-order high-rank (R_n=12) · HSI 3-order, high-rank (R_n=12), missing rate 0.9 | RSE: 0.06695 -> 0.06548 | Helped |
| Use TRLRF instead of TRALS | HSI completion, 3-order low-rank (R_n=8) · HSI 3-order, low-rank (R_n=8), missing rate 0.9 | RSE: 0.1245 -> 0.1166 | Helped |
| Use TRLRF instead of TRWOPT | HSI completion, 5-order high-rank (R_n=22) · HSI 5-order, high-rank (R_n=22), missing rate 0.9 | RSE: 0.1200 -> 0.1035 | Helped |
| Use TRLRF instead of TRWOPT | HSI completion, 5-order low-rank (R_n=18) · HSI 5-order, low-rank (R_n=18), missing rate 0.9 | RSE: 0.1072 -> 0.1062 | Helped |
| Use TRLRF instead of TRWOPT | HSI completion, 8-order high-rank (R_n=24) · HSI 8-order, high-rank (R_n=24), missing rate 0.9 | RSE: 0.1319 -> 0.1190 | Helped |
| Use TRLRF instead of TRWOPT | HSI completion, 8-order low-rank (R_n=20) · HSI 8-order, low-rank (R_n=20), missing rate 0.9 | RSE: 0.1767 -> 0.1421 | Helped |
| Lower the TR-rank from 12 to 8 (ablation) | TRLRF · HSI 3-order, missing rate 0.9 | RSE: 0.06548 -> 0.1166 | Hurt |
| Increase the TR-rank from 18 to 22 (ablation) | TRLRF · HSI 5-order, missing rate 0.9 | RSE: 0.1062 -> 0.1035 | Hurt |
| Increase the TR-rank from 20 to 24 (ablation) | TRLRF · HSI 8-order, missing rate 0.9 | RSE: 0.1421 -> 0.1190 | Hurt |
Closest papers by meaning
- Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm (AAAI 2017 · similarity 0.91)
- Fast Tensor Completion via Approximate Richardson Iteration (ICML 2025 · similarity 0.91)
- Fully-Connected Tensor Network Decomposition and Its Application to Higher-Order Tensor Completion (AAAI 2021 · similarity 0.91)
- A Dual Framework for Low-rank Tensor Completion (NeurIPS 2018 · similarity 0.91)
- Towards Multi-Mode Outlier Robust Tensor Ring Decomposition (AAAI 2024 · similarity 0.89)
- Efficient Leverage Score Sampling for Tensor Train Decomposition (NeurIPS 2024 · similarity 0.87)
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