Research map / Learning theory and algorithms / Matrix and tensor methods
Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams.
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the single row-space sketch with two-sided row- and column-space sketches (ablation) | Tensor-based low-rank approximation · HSI | test error: 0.020 -> 0.069 | Hurt |
| Replace the single row-space sketch with two-sided row- and column-space sketches (ablation) | Tensor-based low-rank approximation · Logo | test error: 0.011 -> 0.015 | Hurt |
| Replace the single row-space sketch with two-sided row- and column-space sketches (ablation) | Tensor-based low-rank approximation · MRI | test error: 0.015 -> 0.024 | Hurt |
| Use two-sided sketches instead of the single-sided tensor-based method (ablation) | Tensor-based low-rank approximation · HSI | training time: 0.53 -> 0.39 | Helped |
| Use two-sided sketches instead of the single-sided tensor-based method (ablation) | Tensor-based low-rank approximation · Logo | training time: 4.76 -> 1.36 | Helped |
| Use two-sided sketches instead of the single-sided tensor-based method (ablation) | Tensor-based low-rank approximation · MRI | training time: 0.23 -> 0.17 | Helped |
| Increase the training sample ratio from 2% to 80% (ablation) | Tensor-based · HSI | test error: 0.952 | Helped |
| Increase the training sample ratio from 2% to 80% (ablation) | Tensor-based · Logo | test error: 0.917 | Helped |
| Increase the training sample ratio from 2% to 80% (ablation) | Tensor-based · MRI | test error: 0.684 | Helped |
| Increase the training sample ratio from 2% to 80% (ablation) | Two-sided tensor-based · HSI | test error: 0.873 | Helped |
| Increase the training sample ratio from 2% to 80% (ablation) | Two-sided tensor-based · Logo | test error: 0.789 | Helped |
| Increase the training sample ratio from 2% to 80% (ablation) | Two-sided tensor-based · MRI | test error: 0.606 | Helped |
Closest papers by meaning
- Few-Shot Data-Driven Algorithms for Low Rank Approximation (NeurIPS 2021 · similarity 0.90)
- Precise expressions for random projections: Low-rank approximation and randomized Newton (NeurIPS 2020 · similarity 0.89)
- Near Optimal Sketching of Low-Rank Tensor Regression (NeurIPS 2017 · similarity 0.89)
- Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model (ICLR 2024 · similarity 0.86)
- Matrix Product Sketching via Coordinated Sampling (ICLR 2025 · similarity 0.86)
- Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning (NeurIPS 2024 · similarity 0.85)
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