Research map / Learning theory and algorithms / Matrix and tensor methods
Convex Co-Embedding for Matrix Completion with Predictive Side Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use convex co-embedding with side-information prediction instead of Maxide | matrix completion · Amazon Beauty | MAP@5 (%): 19.8± 0.4 -> 21.1± 0.2 | Helped |
| Use convex co-embedding instead of Maxide | matrix completion · Amazon Office | MAP@5 (%): 20.6± 0.4 -> 23.0± 0.5 | Helped |
| Use convex co-embedding instead of Maxide | matrix completion · Amazon Sports | MAP@10 (%): 22.2± 0.2 -> 23.8± 0.4 | Helped |
| Use convex co-embedding instead of Maxide/IMC/DirtyIMC | incomplete multi-label learning · 10 Yahoo datasets | AP and AUC | Helped |
Closest papers by meaning
- High Rank Matrix Completion With Side Information (AAAI 2018 · similarity 0.89)
- Generalization Analysis of Deep Non-linear Matrix Completion (ICML 2024 · similarity 0.87)
- Matrix Completion with Incomplete Side Information via Orthogonal Complement Projection (ICML 2025 · similarity 0.86)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.86)
- Multi-Label Ranking Loss Minimization for Matrix Completion (AAAI 2025 · similarity 0.85)
- Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion (NeurIPS 2024 · similarity 0.85)
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