Principal Component Analysis: Dimension Reduction

This lecture covers the concept of Principal Component Analysis (PCA) for dimension reduction in biological data. Starting with the motivation behind dimension reduction, the instructor explains how PCA helps in visualizing and identifying patterns in high-dimensional data. The lecture then delves into the mathematical details of PCA, including covariance calculation and data centering. The process of projecting data onto a lower-dimensional space is illustrated using examples of two-dimensional data. The lecture concludes with a discussion on matrix factorization and the transformation of data using PCA.