Symmetric Nonnegative Matrix Factorization

Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique used to approximate a symmetric matrix, often representing relationships between data points, as the product of a nonnegative low-rank matrix and its transpose. Current research focuses on developing faster and more scalable algorithms, including randomized methods and neural network approaches like SymNMF-Net, as well as incorporating constraints to improve accuracy and address limitations of existing models, such as handling incomplete or high-dimensional data. These advancements aim to enhance the performance and applicability of SymNMF in various fields, particularly in clustering tasks and the analysis of large-scale networks.