Comprehensive overview of dimensionality reduction techniques including PCA, t-SNE, and LDA. Historical context, mathematical models, practical applications, examples, and related concepts.
Detailed exploration of Feature Extraction, including historical context, methodologies, applications, and significance in various fields such as data science, machine learning, and artificial intelligence.
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.
Factor Analysis is a mathematical procedure used to reduce a large amount of data into a simpler structure that can be more easily studied by summarizing information contained in numerous variables into a smaller number of interrelated factors.
