A New Theory for Matrix Completion
Authors: Guangcan Liu, Qingshan Liu, Xiaotong Yuan
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the superiorities of the nonconvex matrix completion methods over the convex program (2), we would like to experiment with randomly generated matrices. We generate a collection of m n (m = n = 100) target matrices according to the model of L0 = BC, where B Rm r0 and C Rr0 n are N(0, 1) matrices. ... For each pair of (r0, |Ω|/(mn)), we run 20 trials, resulting in 8000 simulations in total. ... Figure 2 compares the bilinear program (9) to the convex method (2). |
| Researcher Affiliation | Academia | Guangcan Liu Qingshan Liu Xiao-Tong Yuan B-DAT, School of Information & Control, Nanjing Univ Informat Sci & Technol NO 219 Ningliu Road, Nanjing, Jiangsu, China, 210044 {gcliu,qsliu,xtyuan}@nuist.edu.cn |
| Pseudocode | No | The paper does not include a section explicitly labeled "Pseudocode" or "Algorithm", nor does it present any structured code-like blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper states: "We generate a collection of m n (m = n = 100) target matrices according to the model of L0 = BC, where B Rm r0 and C Rr0 n are N(0, 1) matrices." This indicates synthetic data generation rather than the use of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating synthetic data and running trials with varying parameters (rank, observation fraction) to evaluate the method. It does not mention traditional train/validation/test splits, which are typically used with fixed datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | We generate a collection of m n (m = n = 100) target matrices according to the model of L0 = BC, where B Rm r0 and C Rr0 n are N(0, 1) matrices. The rank of L0, i.e., r0, is configured as r0 = 1, 5, 10, , 90, 95. ... The observation fraction is set to be |Ω|/(mn) = 0.01, 0.05, , 0.9, 0.95. For each pair of (r0, |Ω|/(mn)), we run 20 trials... Here the success is in a sense that PSNR 40d B... When p = m and the identity matrix is used to initialize the dictionary A... |