Principal Component Analysis
Principal Component Analysis, often abbreviated as PCA, is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This technique is commonly used in data analysis, image processing, and machine learning as a method for dimensionality reduction, which helps to improve computational efficiency by reducing the number of features that need to be analyzed while still retaining most of the important information. By identifying patterns in the data, PCA can reveal underlying relationships between variables, making it a valuable tool in exploratory data analysis and predictive modeling. As a subcategory under Machine Learning Methods, PCA is an essential technique for handling high-dimensional data and extracting meaningful insights from complex datasets in the broader context of Technology, Artificial Intelligence, and Artificial Intelligence Algorithms.