{"product_id":"nonnegative-matrix-and-tensor-factorizations-applications-to-exploratory-multi-way-data-analysis-and-blind-source-separation-9780470746660","title":"Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation","description":"This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF's various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF\/NTF and their extensions are increasingly used as tools in signal and image processing, and data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF can provide meaningful components with physical interpretations; for example, in bioinformatics, NMF and its extensions have been successfully applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining. As such, the authors focus on the algorithms that are most useful in practice, looking at the fastest, most robust, and suitable for large-scale models. \u003cp\u003e\u003cb\u003eKey features: \u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eActs 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.\u003c\/li\u003e \u003cli\u003eUses 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.\u003c\/li\u003e \u003cli\u003eProvides a comparative analysis of the different methods in order to identify approximation error and complexity.\u003c\/li\u003e \u003cli\u003eIncludes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eAndrzej Cichocki, Laboratory for Advanced Brain Signal Processing, Riken Brain Science Institute, Japan\u003c\/strong\u003e\u003cbr\u003eProfessor 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 \u003cem\u003eAdaptive Blind Signal and Image Processing\u003c\/em\u003e with Professor Shun-ichi Amari (Wiley, 2002). He is Editor-in-Chief of \u003cem\u003eInternational Journal Computational Intelligence and Neuroscience\u003c\/em\u003e and Associate Editor of \u003cem\u003eIEEE Transactions on Neural Networks\u003c\/em\u003e. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eShun-ichi Amari, Laboratory for Mathematical Neuroscience, Riken Brain Science Institute, Japan\u003c\/strong\u003e\u003cbr\u003eProfessor 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 \u003cem\u003eApplied Intelligence, Journal of Mathematical Systems and Control and Annals of Institute of Statistical Mathematics\u003c\/em\u003e. He is the co-author of three books, and more than three hundred technical papers. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRafal Zdunek, Institute of Telecommunications, Teleinformatics and Acoustics, Wroclaw University of Technology, Poland\u003c\/strong\u003e \u003c\/p\u003e\u003cp\u003eAssociate 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, \u003cem\u003eComputational Intelligence and Neuroscience\u003c\/em\u003e (published May 08). \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eAnh Huy Phan, \u003c\/strong\u003e \u003cstrong\u003eLaboratory for Advanced Brain Signal Processing, Riken Brain Science Institute, Japan\u003c\/strong\u003e\u003cbr\u003eAnh Huy Phan is a researcher at the Laboratory for Advanced Brian Signal Processing at the Riken Brain Science Institute.\u003cbr\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":50324762722578,"sku":"9780470746660","price":176.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_90f27243-5766-4035-a4f7-6c97c946ce55.jpg?v=1727673113","url":"https:\/\/surprise-castle.myshopify.com\/products\/nonnegative-matrix-and-tensor-factorizations-applications-to-exploratory-multi-way-data-analysis-and-blind-source-separation-9780470746660","provider":"Surprise Castle","version":"1.0","type":"link"}