Non Negative Matrix Factorization Techniques

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Non-negative Matrix Factorization Techniques

Ganesh R. Naik
Non negative Matrix Factorization Techniques Book PDF

Author : Ganesh R. Naik
Publisher : Springer
Page : 194 pages
File Size : 53,5 Mb
Release : 2015-09-25
Category : Technology & Engineering
ISBN : 9783662483312

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Non-negative Matrix Factorization Techniques by Ganesh R. Naik Pdf

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.

Non-negative Matrix Factorization Techniques

Ganesh R. Naik
Non negative Matrix Factorization Techniques Book PDF

Author : Ganesh R. Naik
Publisher : Springer
Page : 0 pages
File Size : 42,9 Mb
Release : 2015-10-05
Category : Technology & Engineering
ISBN : 3662483300

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Non-negative Matrix Factorization Techniques by Ganesh R. Naik Pdf

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.

Nonnegative Matrix Factorization

Nicolas Gillis
Nonnegative Matrix Factorization Book PDF

Author : Nicolas Gillis
Publisher : SIAM
Page : 376 pages
File Size : 49,7 Mb
Release : 2020-12-18
Category : Mathematics
ISBN : 9781611976410

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Nonnegative Matrix Factorization by Nicolas Gillis Pdf

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.

Computational Genomics with R

Altuna Akalin
Computational Genomics with R Book PDF

Author : Altuna Akalin
Publisher : CRC Press
Page : 462 pages
File Size : 48,9 Mb
Release : 2020-12-16
Category : Mathematics
ISBN : 9781498781862

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Computational Genomics with R by Altuna Akalin Pdf

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Intelligent Data Analysis

Michael R. Berthold,David J Hand
Intelligent Data Analysis Book PDF

Author : Michael R. Berthold,David J Hand
Publisher : Springer
Page : 515 pages
File Size : 50,9 Mb
Release : 2007-06-07
Category : Computers
ISBN : 9783540486251

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Intelligent Data Analysis by Michael R. Berthold,David J Hand Pdf

This second and revised edition contains a detailed introduction to 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. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.

Machine Learning for Adaptive Many-Core Machines - A Practical Approach

Noel Lopes,Bernardete Ribeiro
Machine Learning for Adaptive Many Core Machines A Practical Approach Book PDF

Author : Noel Lopes,Bernardete Ribeiro
Publisher : Springer
Page : 251 pages
File Size : 49,9 Mb
Release : 2014-06-28
Category : Technology & Engineering
ISBN : 9783319069388

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Machine Learning for Adaptive Many-Core Machines - A Practical Approach by Noel Lopes,Bernardete Ribeiro Pdf

The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.

Nonnegative Matrix and Tensor Factorizations

Andrzej Cichocki,Rafal Zdunek,Anh Huy Phan,Shun-ichi Amari
Nonnegative Matrix and Tensor Factorizations Book PDF

Author : Andrzej Cichocki,Rafal Zdunek,Anh Huy Phan,Shun-ichi Amari
Publisher : John Wiley & Sons
Page : 500 pages
File Size : 45,7 Mb
Release : 2009-07-10
Category : Science
ISBN : 0470747285

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Nonnegative Matrix and Tensor Factorizations by Andrzej Cichocki,Rafal Zdunek,Anh Huy Phan,Shun-ichi Amari Pdf

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.

Matrix and Tensor Factorization Techniques for Recommender Systems

Panagiotis Symeonidis,Andreas Zioupos
Matrix and Tensor Factorization Techniques for Recommender Systems Book PDF

Author : Panagiotis Symeonidis,Andreas Zioupos
Publisher : Springer
Page : 102 pages
File Size : 51,6 Mb
Release : 2017-01-29
Category : Computers
ISBN : 9783319413570

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Matrix and Tensor Factorization Techniques for Recommender Systems by Panagiotis Symeonidis,Andreas Zioupos Pdf

This book presents the algorithms used to provide recommendations by exploiting matrix factorization and tensor decomposition techniques. It highlights well-known decomposition methods for recommender systems, such as Singular Value Decomposition (SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the pros and cons of each method for matrices and tensors. This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods. It also contains two chapters, where different matrix and tensor methods are compared experimentally on real data sets, such as Epinions, GeoSocialRec, Last.fm, BibSonomy, etc. and provides further insights into the advantages and disadvantages of each method. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in both recommenders and factorization methods. Lecturers can also use it for classes on data mining, recommender systems and dimensionality reduction methods.

Computational Intelligence and Intelligent Systems

Zhenhua Li,Xiang Li,Yong Liu,Zhihua Cai
Computational Intelligence and Intelligent Systems Book PDF

Author : Zhenhua Li,Xiang Li,Yong Liu,Zhihua Cai
Publisher : Springer
Page : 640 pages
File Size : 41,8 Mb
Release : 2012-10-06
Category : Computers
ISBN : 9783642342899

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Computational Intelligence and Intelligent Systems by Zhenhua Li,Xiang Li,Yong Liu,Zhihua Cai Pdf

This book constitutes the refereed proceedings of the 6th International Symposium on Intelligence Computation and Applications, ISICA 2012, held in Wuhan, China, in October 2012. The 72 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; combinatorial and numerical optimization; communications and computer networks; data mining; evolutionary multi-objective and dynamic optimization; intelligent computation, intelligent learning systems; neural networks; real-world applications.

Independent Component Analysis and Signal Separation

Tulay Adali,Christian Jutten,Joao Marcos Travassos Romano,Allan Kardec Barros
Independent Component Analysis and Signal Separation Book PDF

Author : Tulay Adali,Christian Jutten,Joao Marcos Travassos Romano,Allan Kardec Barros
Publisher : Springer
Page : 785 pages
File Size : 47,8 Mb
Release : 2009-03-16
Category : Computers
ISBN : 9783642005992

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Independent Component Analysis and Signal Separation by Tulay Adali,Christian Jutten,Joao Marcos Travassos Romano,Allan Kardec Barros Pdf

This book constitutes the refereed proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, held in Paraty, Brazil, in March 2009. The 97 revised papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on theory, algorithms and architectures, biomedical applications, image processing, speech and audio processing, other applications, as well as a special session on evaluation.

Handbook of Research on Recent Developments in Electrical and Mechanical Engineering

Zbitou, Jamal,Pruncu, Catalin Iulian,Errkik, Ahmed
Handbook of Research on Recent Developments in Electrical and Mechanical Engineering Book PDF

Author : Zbitou, Jamal,Pruncu, Catalin Iulian,Errkik, Ahmed
Publisher : IGI Global
Page : 553 pages
File Size : 46,8 Mb
Release : 2019-09-27
Category : Technology & Engineering
ISBN : 9781799801184

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Handbook of Research on Recent Developments in Electrical and Mechanical Engineering by Zbitou, Jamal,Pruncu, Catalin Iulian,Errkik, Ahmed Pdf

Technological advancements continue to enhance the field of engineering and have led to progress in branches that include electrical and mechanical engineering. These technologies have allowed for more sophisticated circuits and components while also advancing renewable energy initiatives. With increased growth in these fields, there is a need for a collection of research that details the variety of works being studied in our globalized world. The Handbook of Research on Recent Developments in Electrical and Mechanical Engineering is a pivotal reference source that discusses the latest advancements in these engineering fields. Featuring research on topics such as materials manufacturing, microwave photons, and wireless power transfer, this book is ideally designed for graduate students, researchers, engineers, manufacturing managers, and academicians seeking coverage on the works and experiences achieved in electrical and mechanical engineering.

Proceedings of the Fourth SIAM International Conference on Data Mining

Michael W. Berry
Proceedings of the Fourth SIAM International Conference on Data Mining Book PDF

Author : Michael W. Berry
Publisher : SIAM
Page : 556 pages
File Size : 47,5 Mb
Release : 2004-01-01
Category : Mathematics
ISBN : 0898715687

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Proceedings of the Fourth SIAM International Conference on Data Mining by Michael W. Berry Pdf

The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.

Audio Source Separation and Speech Enhancement

Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
Audio Source Separation and Speech Enhancement Book PDF

Author : Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
Publisher : John Wiley & Sons
Page : 517 pages
File Size : 41,8 Mb
Release : 2018-10-22
Category : Technology & Engineering
ISBN : 9781119279891

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Audio Source Separation and Speech Enhancement by Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot Pdf

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.

Advances in Swarm Intelligence, Part II

Ying Tan,Yuhui Shi,Yi Chai,Guoyin Wang
Advances in Swarm Intelligence Part II Book PDF

Author : Ying Tan,Yuhui Shi,Yi Chai,Guoyin Wang
Publisher : Springer Science & Business Media
Page : 611 pages
File Size : 50,8 Mb
Release : 2011-05-26
Category : Computers
ISBN : 9783642215230

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Advances in Swarm Intelligence, Part II by Ying Tan,Yuhui Shi,Yi Chai,Guoyin Wang Pdf

The two-volume set (LNCS 6728 and 6729) constitutes the refereed proceedings of the International Conference on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised full papers presented were carefully reviewed and selected from 298 submissions. The papers are organized in topical sections on theoretical analysis of swarm intelligence algorithms, particle swarm optimization, applications of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, artificial immune system, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy methods, and hybrid algorithms - for part I. Topics addressed in part II are such as multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent systems, data mining methods, machine learning methods, feature selection algorithms, pattern recognition methods, intelligent control, other optimization algorithms and applications, data fusion and swarm intelligence, as well as fish school search - foundations and applications.

Mathematics of Data Science: A Computational Approach to Clustering and Classification

Daniela Calvetti,Erkki Somersalo
Mathematics of Data Science A Computational Approach to Clustering and Classification Book PDF

Author : Daniela Calvetti,Erkki Somersalo
Publisher : SIAM
Page : 199 pages
File Size : 41,6 Mb
Release : 2020-11-20
Category : Mathematics
ISBN : 9781611976373

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Mathematics of Data Science: A Computational Approach to Clustering and Classification by Daniela Calvetti,Erkki Somersalo Pdf

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.