advanced-linear-algebra

PCA is an SVD

taught

Principal component analysis, taught in Linear Algebra I via the covariance matrix's eigenvectors, is exactly the SVD of the (centered) data matrix -- the same computation, reached without ever forming the covariance matrix explicitly.

Taught in

PCA is an SVD
Advanced Linear Algebra

Requires

low-rank-approximation-in-practice required
low-rank-structure-when-a-big-matrix-is-really-small required

Required by

Latent structure: recommenders, embeddings, and signals required

Local knowledge map

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