Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm
Authors: Xiawei Guo, Quanming Yao, James Kwok
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed algorithm is more accurate, much faster and more scalable than the state-of-the-art. |
| Researcher Affiliation | Academia | Xiawei Guo, Quanming Yao, James T. Kwok Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay, Hong Kong {xguoae, qyaoaa, jamesk}@cse.ust.hk |
| Pseudocode | Yes | Algorithm 1 Frank-Wolfe (FW) algorithm. Algorithm 2 Reducing the size of {Ud, Σd, Vd}d [D]. Algorithm 3 Fast FW algorithm for tensor completion (FFWTensor). |
| Open Source Code | No | The paper provides links to the code for *other* methods (e.g., Geom CG, Rprecon, ADMM, Fal RTC, TMac, TTN), but does not provide a link or statement about the availability of the source code for their own proposed FFWTensor algorithm. |
| Open Datasets | Yes | The first one is Climate Net8, which is constructed from the 5 5 latitude-longitude gridded climate data set. ... 8http://www.nd.edu/dial/software/climate Net.zip. The second one is You Tube data set9 (Tang, Wang, and Liu 2009), with 15, 088 users and 5 types of Boolean interactions. ... 9http://leitang.net/data/youtube-data.tar.gz |
| Dataset Splits | Yes | 5% of the 2901 3000 3 data tensor entries are randomly sampled as the training set, another 20% as validation set (for parameter tuning), and the rest as testing set. (Color Image Inpainting) 5% of the entries in the 1773 1773 7 data tensor are sampled as observed, another 20% for validation and the rest for testing. (Climate Net) From the 15088 15088 5 data tensor, we randomly sample 0.8% of the entries as observed, another 0.1% for validation and 0.15% for testing. (You Tube full set) From the 1509 1509 5 data tensor, we sample 5% of the entries as observed, another 20% for validation, and the rest for testing. (You Tube subset) |
| Hardware Specification | Yes | Experiments are performed on a PC with Intel i7 CPU and 16GB RAM. |
| Software Dependencies | No | The paper states 'All the codes are in Matlab.' but does not specify particular Matlab versions or any other software libraries with version numbers. |
| Experiment Setup | Yes | We use the same stopping criterion that allows each to run for 1000 seconds. (General Experiments) which is triggered when the size exceeds K = 100. (Basis Reduction) 0.6% of the entries in A are randomly sampled as observed, 0.1% for validation, and another 0.1% for testing. (Synthetic Data) |