Tucker Decomposition is a mathematical technique used in tensor analysis, which generalizes matrix factorization to higher-dimensional data. It breaks down a tensor into a core tensor and a set of factor matrices, allowing for a more manageable representation of complex data structures. This method is particularly useful in fields like machine learning and signal processing. The core tensor captures the interactions between different modes of the original tensor, while the factor matrices represent the relationships within each mode. By simplifying the data, Tucker Decomposition enables efficient computations and helps uncover underlying patterns in multi-dimensional datasets.