Research map / Learning theory and algorithms / Matrix and tensor methods
A Sparse Interactive Model for Matrix Completion with Side Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Sparse interactive model (G with L1 penalty, nuclear norm on E) instead of low-rank-constrained G methods | matrix completion with side information · MovieLens and NCI-DREAM (RMSE, 20-50% missing) | RMSE on missing entries | Helped |
| Removing low-rank constraint on G (full/high-rank G allowed) in favor of sparse G | interactive model G · Synthetic Experiment I (15x50, 20x140 full row rank side features) | RMSE on missing entries | Helped |
| Sparse interactive model vs. baselines on synthetic data with increasing missing rate | synthetic experiments I, II, III · Synthetic experiments (10-80% missing) | RMSE | Helped |
Closest papers by meaning
- High Rank Matrix Completion With Side Information (AAAI 2018 · similarity 0.93)
- Matrix Completion with Incomplete Side Information via Orthogonal Complement Projection (ICML 2025 · similarity 0.90)
- RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees (NeurIPS 2025 · similarity 0.89)
- A Pairwise Pseudo-likelihood Approach for Matrix Completion with Informative Missingness (NeurIPS 2024 · similarity 0.89)
- Generalization Analysis of Deep Non-linear Matrix Completion (ICML 2024 · similarity 0.88)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.88)
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