Tensor Decompositions

Tensor decompositions are mathematical techniques used to break down complex, multi-dimensional data structures—called tensors—into simpler, smaller components. Imagine a tensor as a multi-dimensional spreadsheet or data cube containing information across various factors. Decomposing it helps identify underlying patterns, relationships, or features within the data. These methods are crucial in fields like machine learning, signal processing, and data analysis, enabling efficient data compression, noise reduction, and insightful feature extraction. In essence, tensor decompositions simplify complex data, making it easier to analyze and interpret while preserving essential information.