Uniform Manifold Approximation and Projection (UMAP) | Dimensionality Reduction Techniques (5/5)

DeepFindr · Beginner ·📐 ML Fundamentals ·2y ago
▬▬ Papers / Resources ▬▬▬ Colab Notebook: https://colab.research.google.com/drive/1n_kdyXsA60djl-nTSUxLQTZuKcxkMA83?usp=sharing Sources: - TDA Introduction: https://www.frontiersin.org/articles/10.3389/frai.2021.667963/full - TDA Blogpost: https://chance.amstat.org/2021/04/topological-data-analysis/ - TDA Applications Blogpost: https://orbyta.it/tda-in-a-nutshell-how-can-we-find-multidimensional-voids-and-explore-the-black-boxes-of-deep-learning/ - TDA Intro Paper: https://arxiv.org/pdf/2006.03173.pdf - Mathematical UMAP Blogpost: https://topos.site/blog/2024-04-05-understanding-umap/ - UMAP Author Talk: https://www.youtube.com/watch?v=nq6iPZVUxZU&ab_channel=Enthought - UMAP vs. t-SNE Global preservation paper: https://dkobak.github.io/pdfs/kobak2021initialization.pdf - Fuzzy Topology Slidedeck: https://speakerdeck.com/lmcinnes/umap-uniform-manifold-approximation-and-projection-for-dimension-reduction?slide=39 - Short UMAP Tutorial: https://jyopari.github.io/umap.html Image Sources: - Thumbnail Image: https://johncarlosbaez.wordpress.com/2020/02/10/the-category-theory-behind-umap/ - Persistent Homology: https://orbyta.it/tda-in-a-nutshell-how-can-we-find-multidimensional-voids-and-explore-the-black-boxes-of-deep-learning/ ▬▬ Support me if you like 🌟 ►Link to this channel: https://bit.ly/3zEqL1W ►Support me on Patreon: https://bit.ly/2Wed242 ►Buy me a coffee on Ko-Fi: https://bit.ly/3kJYEdl ►E-Mail: deepfindr@gmail.com ▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬ Music from #Uppbeat (free for Creators!): https://uppbeat.io/t/sulyya/weather-compass License code: ZRGIWRHMLMZMAHQI ▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬ All Icons are from flaticon: https://www.flaticon.com/authors/freepik ▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬ 00:00 Introduction 00:32 Local vs. Global Technqiues 1:25 Is UMAP better? 02:08 The Paper 02:40 Topological Data Analysis Primer 04:04 Simplices 05:04 Filtration 06:22 Persistent Homology 07:02 UMAP Overview 07:40 Step 1: Graph construction 08:25 Uniform distribution 09:44 Non-uniform
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 ▶ Uniform Manifold Approximation and Projection (UMAP) | Dimensionality Reduction Techniques (5/5)
Uniform Manifold Approximation and Projection (UMAP) | Dimensionality Reduction Techniques (5/5)
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Chapters (12)

Introduction
0:32 Local vs. Global Technqiues
1:25 Is UMAP better?
2:08 The Paper
2:40 Topological Data Analysis Primer
4:04 Simplices
5:04 Filtration
6:22 Persistent Homology
7:02 UMAP Overview
7:40 Step 1: Graph construction
8:25 Uniform distribution
9:44 Non-uniform
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