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
Related Courses
Explorez vos données avec des algorithmes non supervisésCentraleSupélec via OpenClassrooms Unsupervised Deep Learning in Python
Udemy Unsupervised Learning in Python
DataCamp Dimensionality Reduction in Python
DataCamp Advanced Dimensionality Reduction in R
DataCamp