Research map / Learning theory and algorithms / Matrix and tensor methods
Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add post-processing (refit basis weights without nuclear norm constraint) (ablation) | FFWTensor · Synthetic data | test RMSE: 0.120 -> 0.100 | Helped |
| add basis reduction (ablation) | FFWTensor · Synthetic data | test RMSE: 0.121 -> 0.100 | Helped |
| exploit sparsity of observed tensor (ablation) | FFWTensor · Synthetic data | feasibility (memory) | Helped |
| FW with scaled latent nuclear norm instead of tensor trace norm FW (TTN) | tensor completion · Color image inpainting | test RMSE: 0.297 ± 0.002 -> 0.186 ± 0.001 | Helped |
| FFWTensor instead of GeomCG | tensor completion · ClimateNet | test AUC: 0.873 -> 0.877 | Helped |
Closest papers by meaning
- Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations (ICML 2019 · similarity 0.91)
- Low-Rank Tensor Completion With a New Tensor Nuclear Norm Induced by Invertible Linear Transforms (CVPR 2019 · similarity 0.91)
- Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion (AAAI 2019 · similarity 0.91)
- Fast Tensor Completion via Approximate Richardson Iteration (ICML 2025 · similarity 0.91)
- A Dual Framework for Low-rank Tensor Completion (NeurIPS 2018 · similarity 0.91)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.87)
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