PCA: Key Concepts

This lecture covers the key concepts of Principal Component Analysis (PCA), a technique used to reduce the dimensionality of data and extract features. PCA utilizes existing correlations across data points to achieve dimensionality reduction and feature extraction. It can be applied as a compression method for data storage, preprocessing method for classification, and to reduce computational costs. Exercises demonstrate how PCA works in practice, including reducing the dimensionality of datasets, preprocessing for classification, and interpreting PCA projections and eigenvectors. The lecture also discusses PCA applied to images and the importance of choosing the optimal number of eigenvectors to minimize information loss.