Nonnegative Matrix And Tensor Factorizations
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Nonnegative Matrix And Tensor Factorizations
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Author : Andrzej Cichocki
language : en
Publisher: John Wiley & Sons
Release Date : 2009-07-10
Nonnegative Matrix And Tensor Factorizations written by Andrzej Cichocki and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-10 with Science categories.
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. 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.
Advances In Nonnegative Matrix And Tensor Factorization
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Author : Andrzej Cichocki
language : en
Publisher:
Release Date : 2008
Advances In Nonnegative Matrix And Tensor Factorization written by Andrzej Cichocki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Nonnegative Matrix Factorization
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Author : Nicolas Gillis
language : en
Publisher: SIAM
Release Date : 2020-12-18
Nonnegative Matrix Factorization written by Nicolas Gillis and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-18 with Mathematics categories.
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.
Nonnegative Matrix And Tensor Factorizations Least Squares Problems And Applications
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Author : Jingu Kim
language : en
Publisher:
Release Date : 2011
Nonnegative Matrix And Tensor Factorizations Least Squares Problems And Applications written by Jingu Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computer science categories.
Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data in which each element has a nonnegative value, and it provides a low-rank approximation formed by factors whose elements are also nonnegative. The nonnegativity constraints imposed on the low-rank factors not only enable natural interpretation but also reveal the hidden structure of data. Extending the benefits of NMF to multidimensional arrays, nonnegative tensor factorization (NTF) has been shown to be successful in analyzing complicated data sets. Despite the success, NMF and NTF have been actively developed only in the recent decade, and algorithmic strategies for computing NMF and NTF have not been fully studied. In this thesis, computational challenges regarding NMF, NTF, and related least squares problems are addressed.
Audio Source Separation And Speech Enhancement
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Author : Emmanuel Vincent
language : en
Publisher: John Wiley & Sons
Release Date : 2018-10-22
Audio Source Separation And Speech Enhancement written by Emmanuel Vincent and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-22 with Technology & Engineering categories.
Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting with sensor array processing, computational auditory scene analysis, and machine learning based approaches such as independent component analysis, respectively. This book is the first one to provide a comprehensive overview by presenting the common foundations and the differences between these techniques in a unified setting. Key features: Consolidated perspective on audio source separation and speech enhancement. Both historical perspective and latest advances in the field, e.g. deep neural networks. Diverse disciplines: array processing, machine learning, and statistical signal processing. Covers the most important techniques for both single-channel and multichannel processing. This book provides both introductory and advanced material suitable for people with basic knowledge of signal processing and machine learning. Thanks to its comprehensiveness, it will help students select a promising research track, researchers leverage the acquired cross-domain knowledge to design improved techniques, and engineers and developers choose the right technology for their target application scenario. It will also be useful for practitioners from other fields (e.g., acoustics, multimedia, phonetics, and musicology) willing to exploit audio source separation or speech enhancement as pre-processing tools for their own needs.
Nonnegative Matrix And Tensor Factorizations
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Author : Andrzej Cichocki
language : en
Publisher: Wiley
Release Date : 2009-10-12
Nonnegative Matrix And Tensor Factorizations written by Andrzej Cichocki and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-12 with Science categories.
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. 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.
Mathematics Of Data Science
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Author : Daniela Calvetti
language : en
Publisher: SIAM
Release Date : 2020-11-20
Mathematics Of Data Science written by Daniela Calvetti and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-20 with Mathematics categories.
This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way. Mathematics of Data Science: A Computational Approach to Clustering and Classification proposes different ways of visualizing high-dimensional data to unveil hidden internal structures, and nearly every chapter includes graphical explanations and computed examples using publicly available data sets to highlight similarities and differences among the algorithms. This self-contained book is geared toward advanced undergraduate and beginning graduate students in the mathematical sciences, engineering, and computer science and can be used as the main text in a semester course. Researchers in any application area where data science methods are used will also find the book of interest. No advanced mathematical or statistical background is assumed.
Intelligent Data Analysis
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Author : Michael R. Berthold
language : en
Publisher: Springer
Release Date : 2007-06-07
Intelligent Data Analysis written by Michael R. Berthold and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-07 with Computers categories.
This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.
Data Mining Foundations And Intelligent Paradigms
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Author : Dawn E. Holmes
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-09
Data Mining Foundations And Intelligent Paradigms written by Dawn E. Holmes and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-09 with Computers categories.
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Neural Information Processing
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Author : Chi-Sing Leung
language : en
Publisher: Springer
Release Date : 2009-12-15
Neural Information Processing written by Chi-Sing Leung and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-15 with Computers categories.
th This two-volume set constitutes the Proceedings of the 16 International Conference on Neural Information Processing (ICONIP 2009), held in Bangkok, Thailand, during December 1–5, 2009. ICONIP is a world-renowned international conference that is held annually in the Asia-Pacific region. This prestigious event is sponsored by the Asia Pacific Neural Network Assembly (APNNA), and it has provided an annual forum for international researchers to exchange the latest ideas and advances in neural networks and related discipline. The School of Information Technology (SIT) at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand was the proud host of ICONIP 2009. The conference theme was “Challenges and Trends of Neural Information Processing,” with an aim to discuss the past, present, and future challenges and trends in the field of neural information processing. ICONIP 2009 accepted 145 regular session papers and 53 special session papers from a total of 466 submissions received on the Springer Online Conference Service (OCS) system. The authors of accepted papers alone covered 36 countries and - gions worldwide and there are over 500 authors in these proceedings. The technical sessions were divided into 23 topical categories, including 9 special sessions.