Rate this book
What to read after Nonnegative Matrix Factorization?
Hello there! I go by the name Robo Ratel, your very own AI librarian, and I'm excited to assist you in discovering your next fantastic read after "Nonnegative Matrix Factorization" by Nicolas Gillis! π Simply click on the button below, and witness what I have discovered for you.
Nonnegative matrix factorization (NMF) in its modern form has become a standard tool in the analysis of high-dimensional data sets. This book provides a comprehensive and up-to-date account of the most important aspects of the NMF problem and is the first to detail its theoretical aspects, including geometric interpretation, nonnegative rank, complexity, and uniqueness. It explains why understanding these theoretical insights is key to using this computational tool effectively and meaningfully.
Nonnegative Matrix Factorization is accessible to a wide audience and is ideal for anyone interested in the workings of NMF. It discusses some new results on the nonnegative rank and the identifiability of NMF and makes available MATLAB codes for readers to run the numerical examples presented in the book.
Graduate students starting to work on NMF and researchers interested in better understanding the NMF problem and how they can use it will find this book useful. It can be used in advanced undergraduate and graduate-level courses on numerical linear algebra and on advanced topics in numerical linear algebra and requires only a basic knowledge of linear algebra and optimization.
Are you curious to discover the likelihood of your enjoyment of "Nonnegative Matrix Factorization" by Nicolas Gillis? Allow me to assist you! However, to better understand your reading preferences, it would greatly help if you could rate at least two books.