PCA & Dimensionality Reduction

High-dimensional data is sparse and hard to visualize. PCA finds the directions of maximum variance, compressing without losing what matters.

Learning Objectives

• →Center data and compute covariance matrix
• →Compute eigenvectors and eigenvalues
• →Project data onto principal components
• →Choose number of components via explained variance

Practice