Dimension Reduction - An Overview
Offered By: Applied Algebraic Topology Network via YouTube
Course Description
Overview
Explore a comprehensive overview of dimension reduction techniques in this 57-minute lecture from the Applied Algebraic Topology Network. Delve into both linear and nonlinear approaches, including principal component analysis (PCA), locally linear embedding (LLE), Laplacian Eigenmaps, Isomap, t-SNE, and UMAP. Examine the application of UMAP, a state-of-the-art tool, to a chemical reaction energy landscape dataset, highlighting the importance of data preprocessing. Gain insights into manifold learning and its various methodologies for uncovering low-dimensional structures in complex datasets. Access accompanying slides for enhanced understanding of the concepts presented.
Syllabus
Bala Krishnamoorthy (10/20/20): Dimension reduction: An overview
Taught by
Applied Algebraic Topology Network
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