Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi‐Way Data Analysis and Blind Source Separation
About this book
Key features:
- Acts as a single source reference guide to NMF, collating information that is widely dispersed in current literature, including the authors’ own recently developed techniques in the subject area.
- Uses generalized cost functions such as Bregman, Alpha and Beta divergences, to present practical implementations of several types of robust algorithms, in particular Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms.
- Provides a comparative analysis of the different methods in order to identify approximation error and complexity.
- Includes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book.
The increasing interest in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential reading for engineers, scientists, researchers, industry practitioners and graduate students across signal and image processing; neuroscience; data mining and data analysis; computer science; bioinformatics; speech processing; biomedical engineering; and multimedia.
Reviews
Author Bios
Andrzej Cichocki, Laboratory for Advanced Brain Signal Processing, Riken
Brain Science Institute, Japan
Professor Cichocki is head of the Laboratory
for Advanced Brain Signal Processing. He has co-authored more than one hundred technical
papers, and is the author of three previous books of which two are published by Wiley.
His most recent book is Adaptive Blind Signal and Image Processing with Professor
Shun-ichi Amari (Wiley, 2002). He is Editor-in-Chief of International Journal
Computational Intelligence and Neuroscience and Associate Editor of IEEE
Transactions on Neural Networks.
Shun-ichi Amari, Laboratory for Mathematical
Neuroscience, Riken Brain Science Institute, Japan
Professor Amari is
head of the Laboratory for Mathematical Neuroscience, as well as vice-president of
the Riken Brain Science Institute. He serves on editorial boards for numerous journals
including Applied Intelligence, Journal of Mathematical Systems and Control and
Annals of Institute of Statistical Mathematics. He is the co-author of three
books, and more than three hundred technical papers.
Rafal Zdunek, Institute of Telecommunications, Teleinformatics and Acoustics, Wroclaw University of Technology, Poland
Associate Professor Zdunek is currently a lecturer at the Wroclaw University of Technology, Poland and up until recently was a visiting research scientist at the Riken Brain Science Institute. He is a member of the IEEE: Signal Processing Society, Communications Society and a member of the Society of Polish Electrical Engineers. Dr Zdunek has guest co-edited with Professor Cichocki amongst others, a special issue on Advances in Non-negative Matrix and Tensor Factorization in the journal, Computational Intelligence and Neuroscience (published May 08).
Anh Huy Phan,
Laboratory for Advanced Brain Signal Processing, Riken Brain Science Institute,
Japan
Anh Huy Phan is a researcher at the Laboratory for Advanced Brian
Signal Processing at the Riken Brain Science Institute.