Parallel Factor Analysis (PARAFAC) is a multi-way decomposition method used to analyze and interpret multi-dimensional data by expressing it as a sum of rank-one tensors. It is widely used in chemometrics, psychometrics, and signal processing to uncover latent structures in complex datasets, ensuring uniqueness in factorization under mild conditions.