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