• Naik, Ganesh R.

Bibliographic Information

Non-negative matrix factorization techniques : advances in theory and applications

Ganesh R. Naik, editor

(Signals and communication technology)

Springer, 2016

  • : hbk

Available at  / 5 libraries

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Includes bibliographical references

Description and Table of Contents


This book collects new results, concepts and further developments of NMF. The open problems discussed include, e.g. in bioinformatics: NMF and its extensions applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining etc. The research results previously scattered in different scientific journals and conference proceedings are methodically collected and presented in a unified form. While readers can read the book chapters sequentially, each chapter is also self-contained. This book can be a good reference work for researchers and engineers interested in NMF, and can also be used as a handbook for students and professionals seeking to gain a better understanding of the latest applications of NMF.

Table of Contents

Theory.- NMF theory and algorithms using Second order and Higher Order statistics.- Multi-layer NMF.- Sparse NMF methods.- Convolutive NMF.- Simultaneous NMF.- Kernel and overlapping NMF.- Large scale NMF.- Projected gradient and Quasi newton NMF algorithms.- Least square algorithms for NMF.- Source Localisation and separation using NMF techniques.- Source Identification issues.- Unknown number of source separation using NMF.- Applications.- NMF for bioinformatics and gene expression applications.- NMF for text mining and sequential analysis.- Neuroscience and biomedical application of NMF.- NMF applications for Brain Computer Interface (BCI) and motor imagery tasks.- NMF for Video and Image processing applications.- Music source separation using NMF methods.- Image, speech and multimedia applications.- Source localisation of Audio and Biosignals.- Source separation in retinal and MRI imaging applications.- Analysis of Heart rate analysis using NMF.- Comparison of real word audio and biosignals with synthetic data using NMF.- NMF for financial and economics applications.- NMF for moving source separation.

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