Matrix Completion with Noisy Entries and Outliers
Authors: Raymond K. W. Wong, Thomas C. M. Lee
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Its promising empirical performance is demonstrated via a sequence of simulation experiments, including image inpainting. [...] Two sets of numerical experiments and a real data application were conducted to evaluate the practical performances of the proposed methodology. |
| Researcher Affiliation | Academia | Raymond K. W. Wong EMAIL Department of Statistics Texas A&M University College Station, TX 77843 USA; Thomas C. M. Lee EMAIL Department of Statistics University of California Davis, CA 95616, USA |
| Pseudocode | Yes | Details of this algorithm based on pseudo data matrix are given in Algorithm 1. [...] Algorithm 1 The General Robust Algorithm; Algorithm 2 Robust-Impute |
| Open Source Code | No | The paper describes two algorithms (Algorithm 1 and Algorithm 2) and evaluates their performance, but does not provide any explicit statement about releasing the source code or a link to a code repository. |
| Open Datasets | Yes | In this experiment the target matrix is the so-called Lena image that has been used by many authors in the image processing literature. [...] In this application the target matrix is an image from a Landsat Thematic Mapper data set publicly available at http://ternauscover.science.uq.edu.au/. |
| Dataset Splits | Yes | To evaluate the recovered matrix, the observed pixels were split into training, validation and testing sets consisting 80%, 10% and 10% of the observed (nonzero) entries respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. It only describes the algorithms and their empirical performance. |
| Software Dependencies | No | The paper mentions implementing algorithms (Algorithm 1 and Algorithm 2) and comparing them to Soft-Impute, but does not specify any programming languages, libraries, or software dependencies with version numbers. |
| Experiment Setup | Yes | For each simulated data set, the target matrix was generated as X0 = UV , where U and V are random matrices of size 100 r with independent standard normal Gaussian entries. Then each entry of X0 is contaminated by additional independent Gaussian noise with standard deviation σ, which is set to a value such that the signal-to-noise ratio (SNR) is 1. [...] In this study, we used two values for r (5, 10), three values for p (0, 0.05, 0.1) and three values for q (0.25, 0.5, 0.75). [...] The average training and testing errors of the recovered images of matrix ranks 50, 75, 100 and 125 are reported in Table 1. |