Summary of Negative Binomial Matrix Completion, by Yu Lu et al.
Negative Binomial Matrix Completion
by Yu Lu, Kevin Bui, Roummel F. Marcia
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Signal Processing (eess.SP); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Matrix completion is a machine learning problem that focuses on recovering missing or incomplete information in matrices. This task has applications in image processing and network analysis. Previous research has proposed Poisson matrix completion for count data with noise following a Poisson distribution, but this assumption may not hold true for overdispersed count data, where the variance is greater than the mean. To address this issue, we propose a nuclear-norm regularized model for negative binomial (NB) matrix completion, which can be solved using proximal gradient descent. Our experiments demonstrate that NB matrix completion outperforms Poisson matrix completion in various noise and missing data settings on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you have a big puzzle with some pieces missing. Matrix completion is a way to fill in those missing pieces so you can see the whole picture again. This problem comes up when working with images, social networks, or other complex systems. In the past, scientists assumed that the missing information followed a certain pattern, but this might not always be true. To make things more accurate, we’ve developed a new way to fill in those missing pieces, called negative binomial matrix completion. Our tests show that this new method is better at solving puzzles with missing information than older methods. |
Keywords
» Artificial intelligence » Gradient descent » Machine learning
