Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation
Authors: David Anderson, Ming Gu
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numeric experiments illustrate that SRLU preserves sparsity, highlights important data features and variables, can be efficiently updated, and calculates data approximations nearly as accurately as possible. To the best of our knowledge this is the first practical variant of the LU factorization for effective and efficient low-rank matrix approximation. |
| Researcher Affiliation | Academia | 1University of California, Berkeley. Correspondence to: David Anderson <EMAIL>, Ming Gu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 The LU factorization; Algorithm 2 Crout LU; Algorithm 3 TRLUCP; Algorithm 4 Spectrum-Revealing Pivoting (SRP) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of release) to the source code for the methodology described. |
| Open Datasets | Yes | We test the spectral norm error of these algorithms on matrices from the sparse matrix collection in (Davis & Hu, 2011). ... The university of florida sparse matrix collection. ACM Transactions on Mathematical Software, 38:1:1 1:25, 2011. URL http://www.cise.ufl.edu/research/sparse/matrices. |
| Dataset Splits | Yes | The data contains 39,861 documents and 28,102 words/terms, and an initial SRLU factorization of rank 20 was performed on the first 30K documents. |
| Hardware Specification | No | All numeric experiments were run on NERSC’s Edison. This is a general name for a supercomputer but does not provide specific hardware details like CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions software like 'LAPACK' and 'PROPACK' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Note that for Subspace Iteration, we choose iteration parameter q = 0 and do not measure the time of applying the random projection... Using tuning parameter f = 5, no swaps were needed in all cases. |