CANDECOMP/PARAFAC (CP) is a mathematical technique used for decomposing multi-dimensional data into simpler, interpretable components. It breaks down a tensor, which is a generalization of matrices to higher dimensions, into a sum of rank-one tensors. This allows researchers to analyze complex datasets, such as those found in chemometrics or image processing, by identifying underlying patterns and relationships. The CP model represents data in a way that highlights the contributions of individual factors, making it easier to visualize and understand. It is particularly useful in fields like psychometrics and signal processing, where multi-way data is common, enabling better insights and decision-making.