Higher-Order Singular Value Decomposition (HOSVD) is an extension of the matrix singular value decomposition to tensors, allowing for the decomposition of multi-dimensional data into simpler, interpretable components. It is widely used in fields such as signal processing, data mining, and machine learning for tasks like dimensionality reduction and feature extraction in multi-way data arrays.