Higher-Order Singular Value Decomposition (HOSVD) is a generalization of the matrix singular value decomposition (SVD) to tensors, allowing for the analysis and decomposition of multi-dimensional data. It is a powerful tool in multi-linear algebra that provides a way to extract meaningful patterns and features from high-dimensional datasets by transforming them into a lower-dimensional space while preserving essential structural information.