Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation
Authors: Jicong Fan6587-6596
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results show that D-NLMC outperforms the baselines in real applications. |
| Researcher Affiliation | Academia | Jicong Fan1,2 1The Chinese University of Hong Kong (Shenzhen) 2Shenzhen Research Institute of Big Data Shenzhen, China fanjicong@cuhk.edu.cn |
| Pseudocode | Yes | Algorithm 1: Fast EVD for Kt |
| Open Source Code | No | The paper does not provide any statement about releasing the source code for its described methodology, nor does it include any links to a code repository. |
| Open Datasets | Yes | We test the proposed method on the SML2010 indoor temperature dataset3 from the UCI machine learning repository. The dataset consists of 2764 samples of 24 variables such as indoor temperature, relative humidity, and lightning. (https://archive.ics.uci.edu/ml/datasets/SML2010) |
| Dataset Splits | No | The paper describes randomly removing fractions of entries or blocks for testing performance and mentions a training period of 20 columns for synthetic data, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'L-BFGS (Liu and Nocedal 1989)' for optimization, but it does not provide specific version numbers for any software, libraries, or frameworks used in the implementation or experimentation. |
| Experiment Setup | Yes | In D-NLMC, we set w = 20, R = 15, and use Gaussian kernel with σ = µw 2 Pw i=1 Pw j=1 xi xj (similar to (Fan, Zhang, and Udell 2020)), where µ is a constant such as 1 or 3. In this case, we use µ = 1. |