Principal Component Analysis Algorithm Principal Component Analysis Concept Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional datasets into a lower-dimensional space while preserving as much of the variance as possible. It works by identifying the principal components that capture the most significant variation in the data and projecting the data onto these components. Suppose we have a dataset of images with 1000 pixels each. We want to reduce the dimensionality of the dataset to 100 pixels to reduce the computational complexity. We use PCA to identify the principal components that capture the most significant variation in the images. We project the images onto these components to obtain a lower-dimensional representation of the images. Principal Component Analysis  Algorithm Define the problem and collect data. Compute the covariance matrix of the data. Compute the eigenvectors and eigenvalues of the covariance matrix. Choos