Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.

Jun 23, 2025 · Principal component analysis (PCA) is a dimensionality reduction technique that transforms a data set into a set of orthogonal components — called principal components — which …

Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the information in the …

Principal component analysis, or PCA, reduces the number of dimensions in large datasets to principal components that retain most of the original information. It does this by transforming potentially …

Principal Component Analysis (PCA) is a widely used statistical method in data analysis and machine learning. It simplifies large and complex datasets by reducing their variables, or dimensions.

Nov 13, 2025 · PCA (Principal Component Analysis) is a dimensionality reduction technique and helps us to reduce the number of features in a dataset while keeping the most important information. It …

PCA, or Principal Component Analysis, is an algorithm to reduce a large data set without loss of important imformation.