Description: Tensor decomposition is a technique used to decompose multidimensional arrays, known as tensors, into simpler components that facilitate their analysis. This technique is fundamental in the field of unsupervised learning, where the goal is to extract underlying patterns and structures in data without the need for labels. By decomposing a tensor, relevant features and relationships between different dimensions of the data can be identified, allowing for better understanding and visualization of the information. Tensor decomposition relies on advanced mathematical methods, such as singular value decomposition (SVD) and tensor factorization, which enable the representation of data in more manageable forms. This technique is particularly useful in contexts where data is complex and multidimensional, such as in image analysis, signal processing, and data mining. By simplifying the structure of the data, tensor decomposition not only improves computational efficiency but also enhances the ability of machine learning algorithms to generalize and make accurate predictions.
