Summary of Two-stage Hierarchical and Explainable Feature Selection Framework For Dimensionality Reduction in Sleep Staging, by Yangfan Deng et al.
Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging
by Yangfan Deng, Hamad Albidah, Ahmed Dallal, Jijun Yin, Zhi-Hong Mao
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
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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 A machine learning-based framework for analyzing EEG signals during sleep stages proposes a two-stage hierarchical feature selection approach to address high-dimensional data challenges. By incorporating feature selection algorithms into dimensionality reduction, this method compensates for structural information loss in traditional analysis. Topological features are extracted from EEG signals using topological data analysis to improve classification accuracy. The proposed framework is evaluated against three dimensionality reduction algorithms: PCA, t-SNE, and UMAP. While t-SNE achieves the highest accuracy (79.8%), UMAP is deemed the optimal choice considering overall performance and computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of looking at brain signals during sleep uses a special kind of math to make sense of really big data sets. This helps researchers better understand how our brains work while we’re sleeping, which is important for keeping us healthy. The method uses something called topological features to help fill in the gaps when analyzing these big data sets. It then compares its results with three other ways of doing things and finds that one approach, called UMAP, works best. This could lead to new discoveries about how our brains work while we’re sleeping. |
Keywords
» Artificial intelligence » Classification » Dimensionality reduction » Feature selection » Machine learning » Pca » Umap
