Tensor decomposition is a mathematical technique used to break down a multi-dimensional array, known as a tensor, into simpler, more manageable components. This process helps in identifying patterns and relationships within complex data structures, making it easier to analyze and interpret. There are various methods for tensor decomposition, such as CANDECOMP/PARAFAC and Tucker decomposition. These methods allow researchers and data scientists to extract meaningful information from large datasets, which can be applied in fields like machine learning, signal processing, and computer vision.