Research map / Learning theory and algorithms / Matrix and tensor methods
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Factorize compressed gene-expression data before sparse recovery | NMF · Gene expression datasets | reconstruction error | Helped |
| Factorize the EEG tensor before sparse recovery | rank-10 non-negative CP decomposition · CHB-MIT EEG at d=1000 | normalized Frobenius error: 0.82 vs. 0.83 | No clear effect |
| Factorize the EEG tensor before sparse recovery rather than recover it first | rank-10 non-negative CP decomposition · CHB-MIT EEG | runtime: three orders of magnitude | Helped |
| Run sparse recovery on factors after compressed-domain NMF instead of recovering every sample first | NMF pipeline · Gene expression datasets | number of sparse recovery calls: r vs. m | Helped |
Closest papers by meaning
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- Robust Tensor Factorization With Unknown Noise (CVPR 2016 · similarity 0.88)
- SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors (AAAI 2021 · similarity 0.88)
- Deep Rank-One Tensor Functional Factorization for Multi-Dimensional Data Recovery (AAAI 2025 · similarity 0.87)
- Non-Convex Tensor Recovery from Local Measurements (AAAI 2025 · similarity 0.87)
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