Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch
Authors: Osman Asif Malik, Stephen Becker
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithms on sparse synthetic data and compare them to multiple other methods. We also apply one of our algorithms to a real dense 38 GB tensor representing a video and use the resulting decomposition to correctly classify frames containing disturbances. |
| Researcher Affiliation | Academia | Osman Asif Malik Department of Applied Mathematics University of Colorado Boulder osman.malik@colorado.edu Stephen Becker Department of Applied Mathematics University of Colorado Boulder stephen.becker@colorado.edu |
| Pseudocode | Yes | Algorithm 1: TUCKER-ALS (aka HOOI), Algorithm 2: TUCKER-TS (proposal), Algorithm 3: TUCKER-TTMTS (proposal) |
| Open Source Code | Yes | A Matlab implementation of our algorithms can be found at https://github.com/Osman Malik/tucker-tensorsketch. |
| Open Datasets | No | The paper mentions using 'sparse synthetic data' and 'a real dense 38 GB tensor representing a video', but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available version of the video dataset. The GitHub link is for code, not the raw video data itself. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | All synthetic results are averages over ten runs in an environment using four cores of an Intel Xeon E5-2680 v3 @2.50GHz CPU and 21 GB of RAM. This work utilized the RMACC Summit supercomputer, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. |
| Software Dependencies | Yes | Our Matlab implementation that we provided a link to at the beginning of Section 3 comes with demo script files for running experiments similar to those presented here. We compare our algorithms to TUCKER-ALS and MET in Tensor Toolbox version 2.6 [3, 19] |
| Experiment Setup | Yes | Unless stated otherwise, we stop after 50 iterations or when the change in G is less than 1e-3. We have found that the choice J1 = KRN 1 and J2 = KRN, for a constant K > 4, works well in practice. ...unless stated otherwise, always use K = 10. For both plots, the tensor size is 500 500 500 with nnz(Y) 1e+6 and true rank (15, 15, 15). The algorithms use a target rank of (10, 10, 10). We add i.i.d. normally distributed noise with standard deviation 1e-3 to all nonzero tensor elements. A convergence tolerance of 1e-1 is used for these experiments. We compute a rank (10, 10, 10) Tucker decomposition of the tensor using TUCKER-TTMTS with the sketch dimension parameter set to K = 100 and a maximum of 30 iterations. |