UMAP Dimension Reduction, Main Ideas!!!
18:52
YouTubeStatQuest with Josh Starmer
UMAP Dimension Reduction, Main Ideas!!!
UMAP is one of the most popular dimension-reductions algorithms and this StatQuest walks you through UMAP, one step at a time, so that you will have a solid understanding of how UMAP works. NOTE: This StatQuest is based on the original UMAP manuscript... https://arxiv.org/pdf/1802.03426.pdf ...specifically Appendix C, From t-SNE to UMAP, which ...
149K viewsMar 7, 2022
Shorts
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PCA, t-SNE, and UMAP are key techniques in bioinformatics and machine learning that simplify complex biological data and reveal hidden patterns. 🔍 PCA: Reduces dimensionality by identifying key features and trends, ideal for data exploration. 📍 t-SNE: Preserves local similarities to uncover clusters and subgroups in non-linear data. 🌐 UMAP: Balances speed and accuracy, maintaining both local and global structures for large, interpretable datasets. These techniques are essential for AI and ML
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PCA, t-SNE, and UMAP are key techniques in bioinformatics and machine learning that simplify complex biological data and reveal hidden patterns. 🔍 PCA: Reduces dimensionality by identifying key features and trends, ideal for data exploration. 📍 t-SNE: Preserves local similarities to uncover clusters and subgroups in non-linear data. 🌐 UMAP: Balances speed and accuracy, maintaining both local and global structures for large, interpretable datasets. These techniques are essential for AI and ML
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