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Dimensionality Reduction: Rank-k Approximation and Eigen-decomposition - Lecture 20

UofU Data Science via YouTube

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Learn advanced dimensionality reduction techniques in this university lecture covering rank-k approximation methods, their connection to eigendecomposition, and the power method algorithm for finding dominant eigenvalues and eigenvectors in large matrices.

Syllabus

FoDA F22 Lecture 20

Taught by

UofU Data Science

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