Summary of Nonnegative Matrix Factorization in Dimensionality Reduction: a Survey, by Farid Saberi-movahed et al.
Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey
by Farid Saberi-Movahed, Kamal Berahman, Razieh Sheikhpour, Yuefeng Li, Shirui Pan
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this comprehensive survey, researchers examine Nonnegative Matrix Factorization (NMF) as a powerful method for dimensionality reduction, exploring its applications in feature extraction and selection. The paper introduces a classification of dimensionality reduction techniques, providing an in-depth summary of various NMF approaches used for feature learning accuracy and reducing training time by eliminating redundant features, noise, and irrelevant data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dimensionality Reduction is important because it helps machines learn better from the information they receive. This means getting rid of things that are not useful or relevant to what we want them to learn. Nonnegative Matrix Factorization (NMF) is a popular way to do this. The researchers in this study looked at how NMF works and how it can be used for different tasks, like finding important features in data. |
Keywords
» Artificial intelligence » Classification » Dimensionality reduction » Feature extraction
