Tucker Decomposition is a form of higher-order principal component analysis that decomposes a tensor into a core tensor multiplied by a matrix along each mode, allowing for dimensionality reduction and data compression. It is widely used in signal processing, psychometrics, and computer vision for analyzing multi-way data arrays and extracting meaningful patterns.