Simple Atom Selection Strategy for Greedy Matrix Completion
Authors: Zebang Shen, Hui Qian, Tengfei Zhou, Song Wang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments of Recommendation and Image Recovery are conducted to make empirical evaluation with promising results. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Zhejiang University, China 2University of South Carolina, U.S.A. |
| Pseudocode | Yes | We summarize our pseudo code in Algorithm 1. Algorithm 2 OA variants: OA-GECO,OA-R1MP, OAER1MP, OA-JS, and OA-BOOST |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We use three largest publicly available datasets: Movie Lens10M, Net Flix, and Yahoo Music to test the matrix completion based recommendation. In Image Recovery, we use five 512 × 512 sized gray-scale benchmark images2. |
| Dataset Splits | Yes | All datasets are randomly split into equal-sized training and testing parts. λ, the regularization parameter for BOOST, is selected by 3-fold cross validation. |
| Hardware Specification | Yes | All experiments are conducted on the same PC (Windows Server 2012 R2, Intel Xeon E5 2690v2*2 CPU, and 128G RAM). |
| Software Dependencies | No | The paper states: We call the PROPACK to solve Top-1 SVD for T1SVD strategy. However, it does not specify a version number for PROPACK or any other software, which is required for reproducibility. |
| Experiment Setup | Yes | As for alternating minimization, we use random and the Approx SV initialization respectively and set the maximum number of iterations to be ten. For the parameter setting, we set the same maximum number of iteration for all the algorithms. And λ, the regularization parameter for BOOST, is selected by 3-fold cross validation. Additionally, JS requires a regularization parameter t, which is set to a doubled value of the nuclear norm solved by OA-ER1MP (This value is close in all OA variants). |