Research map / Learning theory and algorithms / Matrix and tensor methods
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| define TensorSketch operators upfront instead of redefining before each use (ablation) | TUCKER-TS · Synthetic sparse 500x500x500 tensor, true rank 15, target rank 10 | error relative to TUCKER-ALS | Helped |
| define TensorSketch operators upfront instead of redefining before each use (ablation) | TUCKER-TTMTS · Synthetic sparse 500x500x500 tensor, true rank 15, target rank 10 | error relative to TUCKER-ALS | Helped |
| use TUCKER-TTMTS instead of TUCKER-TS | Tucker sketching · Sparse synthetic tensors, Rtrue = R | relative error | Hurt |
| use TUCKER-TTMTS instead of TUCKER-TS when true rank exceeds target rank | Tucker sketching · Sparse synthetic tensors, Rtrue > R | relative error | No clear effect |
| increase sketch dimension constant K (ablation) | TUCKER-TS and TUCKER-TTMTS · Sparse synthetic 500x500x500 | error relative to TUCKER-ALS | Helped |
| use 3 clusters instead of 2 for k-means on time factor (ablation) | TUCKER-TTMTS on 38 GB video · Video frame disturbance classification | classification quality | Helped |
Closest papers by meaning
- Fitting Low-Rank Tensors in Constant Time (NeurIPS 2017 · similarity 0.92)
- Fast and accurate randomized algorithms for low-rank tensor decompositions (NeurIPS 2021 · similarity 0.91)
- Singleshot : a scalable Tucker tensor decomposition (NeurIPS 2019 · similarity 0.90)
- Fast Tensor Completion via Approximate Richardson Iteration (ICML 2025 · similarity 0.88)
- Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · similarity 0.88)
- Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks (NeurIPS 2025 · similarity 0.87)
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