Tensor Factorization

Tensor factorization is a powerful set of techniques used to decompose multi-dimensional data (tensors) into lower-dimensional components, revealing underlying patterns and structures. Current research emphasizes developing efficient algorithms, such as PARAFAC2 and its variants, for handling large-scale, incomplete, and irregularly structured data, often incorporating constraints like non-negativity and sparsity for improved interpretability. These methods find broad application in diverse fields, including natural language processing, recommendation systems, and scientific data analysis, offering improved data compression, imputation of missing values, and enhanced model interpretability.