Code for Principal Component Analysis and Low-Rank Models

Python notebooks for all the computational examples about Principal Component Analysis and Low-Rank Models from Chapter 11 of the book. See here for the full code repository.

Gaussian random vector. Mean of a random vector vector, covariance matrix, directional variance, principal component analysis, spectral theorem
Canadian cities. Sample mean of a vector, sample covariance matrix, principal component analysis, spectral theorem
Faces. Principal component analysis, dimensionality reduction, sample mean of a vector
Wheat seeds. Sample covariance matrix, principal component analysis, dimensionality reduction
Face classification. Principal component analysis, dimensionality reduction, nearest neighbor
Temperatures in the United States. Sample covariance matrix, singular value decomposition, principal component analysis, low-rank model
Prediction of movie ratings (cartoon example). Low rank model, singular value decomposition, matrix completion, collaborative filtering, singular-value thresholding, imputation
Prediction of movie ratings (real data). Low rank model, singular value decomposition, matrix completion, collaborative filtering, singular-value thresholding, imputation
Topic modeling. Low-rank model, singular value decomposition, nonnegative matrix factorization