How Data Scientists Use Manifold Learning for Dimensionality Reduction
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
In episode 87 of The Data Science Podcast, Lucas and Luna explore manifold learning—a powerful technique for dimensionality reduction that goes beyond PCA. They focus on t-SNE and UMAP, explaining how these algorithms preserve local and global structure in high-dimensional data. Using concrete examples from genomics and image datasets, they discuss when to use each method and common pitfalls like misleading visualizations. Lucas shares a cautionary tale about a team that over-interpreted a t-SNE plot, while Luna explains how UMAP scales to millions of points. They also touch on recent developments like parametric UMAP and its integration with deep learning. Perfect for data scientists who want to understand the trade-offs between linear and nonlinear dimensionality reduction. #ManifoldLearning #DimensionalityReduction #tSNE #UMAP #DataScience #MachineLearning #PCA #DataVisualization #TopologicalDataAnalysis #Genomics #ImageData #KLDivergence #CrossEntropy #ParametricUMAP #FexingoBusiness #BusinessPodcast #Technology #DataSciencePodcast Keep every episode free: buymeacoffee.com/fexingo
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