Visualizing High Dimension Data Using UMAP Is A Piece Of Cake Now
Skills:
ML Maths Basics90%
Google colab link: https://colab.research.google.com/drive/1jV4kOHbpdu0Zc7Ml18kdxaQJxV81vB21?usp=sharing
UMAP (Uniform Manifold Approximation and Projection) is a novel
manifold learning technique for dimension reduction. UMAP is constructed
from a theoretical framework based in Riemannian geometry and algebraic
topology. the result is a practical scalable algorithm that is applicable to
real world data. UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with
superior run time performance. Furthermore, UMAP has no computational
restrictions on embedding dimension, making it viable as a general purpose
dimension reduction technique for machine learning
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