Comprehensive overview of dimensionality reduction techniques including PCA, t-SNE, and LDA. Historical context, mathematical models, practical applications, examples, and related concepts.
An in-depth exploration of eigenvalues and eigenvectors, their importance in various mathematical and applied contexts including PCA for dimensionality reduction and solving systems of differential equations.
Principal Components Analysis (PCA) is a linear transformation technique that converts a set of correlated variables into a set of uncorrelated variables called principal components. Each succeeding component accounts for as much of the remaining variability in the data as possible.
