tensor decomposition

Tensor decomposition is a mathematical technique used to simplify complex multi-dimensional data (called tensors) by breaking it down into smaller, more manageable parts. Think of a tensor as a multi-layered spreadsheet with many rows, columns, and possibly other dimensions. Decomposition reveals underlying patterns or structures within this data, making it easier to analyze, compress, or extract meaningful insights. It’s widely used in fields like machine learning, signal processing, and data analysis to handle large, complex datasets efficiently and to identify hidden relationships among the data points.