Negative Matrix Factorisation

Negative Matrix Factorization (NMF) is a technique used to decompose a data matrix into two non-negative matrices, revealing underlying patterns and features. Current research focuses on improving the robustness of NMF algorithms, particularly against noise and outliers, exploring variations like L1, L2, and L2,1-NMF to achieve this. These advancements are crucial for enhancing the reliability of NMF in applications such as image processing and data clustering, where noisy or corrupted data is common. The ultimate goal is to develop more resilient and efficient NMF methods for various real-world applications.