Description: PCA variants (Principal Component Analysis) are methods derived from the original technique that aim to address specific limitations and improve data interpretation in the context of unsupervised learning. PCA is a statistical technique that transforms a set of possibly correlated variables into a set of uncorrelated variables, called principal components, which retain most of the variability present in the data. However, in situations where data exhibit particular characteristics, such as the presence of noise, nonlinear patterns, or the need to preserve local structure, PCA variants emerge. These variants include, among others, robust PCA, which focuses on minimizing the influence of outliers, and nonlinear PCA, which uses techniques like kernel PCA to capture nonlinear relationships in the data. Other variants, such as Sparse PCA, aim to obtain components that are sparse, facilitating interpretation and feature selection. In summary, PCA variants are valuable tools that allow analysts and data scientists to extract meaningful information from complex datasets, adapting to the specific needs of each situation.
