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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.
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