Principal Component Analysis: Introduction

This lecture covers the introduction to Principal Component Analysis (PCA), focusing on finding the standardized linear combinations of original variables with maximal variance. PCA aims to summarize data by identifying uncorrelated linear combinations. The lecture explains the theoretical background, properties of principal components, and their applications in data reduction. The instructor discusses the methodology initiated by Pearson and developed by Hotelling, emphasizing the importance of variance in separating data objects.