Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
Authors: Quanming Yao, James Tin-Yau Kwok, Bo Han
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a number of synthetic and real-world data sets show that the proposed algorithm is more efficient in both time and space, and is also more accurate than existing approaches. |
| Researcher Affiliation | Collaboration | 1 Paradigm Inc, Beijing, China 2 Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong 3 Center for Advanced Intelligence Project, RIKEN, Japan. |
| Pseudocode | Yes | Algorithm 1 NOnconvex Regularized Tensor (NORT). |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We use windows, tree and rice from (Hu et al., 2013),... Experiments are performed on three hyper-spectral data sets (Appendix C.3): Cabbage (1312 432 49), Scene (1312 951 49) and Female (592 409 148)... on the You Tube data set (Lei et al., 2009). |
| Dataset Splits | Yes | We use 50% of them for training, and the remaining 50% for validation. Testing is evaluated on the unobserved elements in O. (Synthetic Data) ... We randomly sample 10% of the pixels for training, which are then corrupted by Gaussian noise N(0, 0.01). Half of the training pixels are used for validation. (Color Images) ... We use 50% of the observations for training, another 25% for validation and the rest for testing. (Social Networks) |
| Hardware Specification | Yes | Experiments are performed on a PC with Intel-i8 CPU and 32GB memory. |
| Software Dependencies | No | The paper mentions implementation in Matlab and C, but does not provide specific version numbers for these software dependencies or any libraries used. |
| Experiment Setup | Yes | For NORT, τ has to be larger than ρ + DL (Corollary 3.6). However, a large τ leads to slow convergence (Remark 3.2). Hence, we set τ = 1.01(ρ+DL). Moreover, we set γ1 = 0.1 and p = 0.5 as in (Li et al., 2017). Besides, Fτ in step 5 of Algorithm 1 is hard to evaluate, and we use F instead as in (Zhong & Kwok, 2014). |