`principal-component-analysis` Related Article Results

Principal Component Analysis (PCA) is a transformation technique that reduces the dimensions of datasets containing many interrelated variables while preserving as much of the variation within the data as possible. These studies were first initiated by Karl Pearson in 1901 and later developed by Hotelling in 1933. The goal is to find the optimal transformation that allows the data to be represented using fewer variables. The variables obtained after transformation are called principal components