Using Algebraic Factorizations for Interpretable Learning - IPAM at UCLA

Explore the fundamental concepts of Non-negative Matrix Factorization (NMF) and its applications in interpretable machine learning through this 49-minute lecture presented by Deanna Needell from the University of California, Los Angeles. Delve into the main principles of NMF, its implementation techniques, and advanced variations such as online and streaming methods. Discover how this powerful mathematical tool can be applied to a wide range of fields, including imaging, medicine, forecasting, and collaborative filtering. Gain insights into how NMF contributes to dictionary learning problems and helps represent complex datasets using a reduced number of extracted features. Engage with the content through discussions and questions, enhancing your understanding of algebraic factorizations and their role in creating more interpretable machine learning models.