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Principal Component Analysis And Randomness Test For Big Data Analysis
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Book Synopsis Principal Component Analysis and Randomness Test for Big Data Analysis by : Mieko Tanaka-Yamawaki
Download or read book Principal Component Analysis and Randomness Test for Big Data Analysis written by Mieko Tanaka-Yamawaki and published by Springer Nature. This book was released on 2023-05-23 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the novel approach of analyzing large-sized rectangular-shaped numerical data (so-called big data). The essence of this approach is to grasp the "meaning" of the data instantly, without getting into the details of individual data. Unlike conventional approaches of principal component analysis, randomness tests, and visualization methods, the authors' approach has the benefits of universality and simplicity of data analysis, regardless of data types, structures, or specific field of science. First, mathematical preparation is described. The RMT-PCA and the RMT-test utilize the cross-correlation matrix of time series, C = XXT, where X represents a rectangular matrix of N rows and L columns and XT represents the transverse matrix of X. Because C is symmetric, namely, C = CT, it can be converted to a diagonal matrix of eigenvalues by a similarity transformation SCS-1 = SCST using an orthogonal matrix S. When N is significantly large, the histogram of the eigenvalue distribution can be compared to the theoretical formula derived in the context of the random matrix theory (RMT, in abbreviation). Then the RMT-PCA applied to high-frequency stock prices in Japanese and American markets is dealt with. This approach proves its effectiveness in extracting "trendy" business sectors of the financial market over the prescribed time scale. In this case, X consists of N stock- prices of length L, and the correlation matrix C is an N by N square matrix, whose element at the i-th row and j-th column is the inner product of the price time series of the length L of the i-th stock and the j-th stock of the equal length L. Next, the RMT-test is applied to measure randomness of various random number generators, including algorithmically generated random numbers and physically generated random numbers. The book concludes by demonstrating two applications of the RMT-test: (1) a comparison of hash functions, and (2) stock prediction by means of randomness, including a new index of off-randomness related to market decline.
Book Synopsis Principal Component Analysis and Randomness Tests for Big Data Analysis by : Mieko Tanaka-Yamawaki
Download or read book Principal Component Analysis and Randomness Tests for Big Data Analysis written by Mieko Tanaka-Yamawaki and published by Springer. This book was released on 2022-09-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the novel approach of analyzing large-sized numerical data (so-called big data). The essence of this approach is to grasp the "meaning" of the data instantly, without getting into the details of individual data. Unlike conventional approaches of principal component analysis, randomness tests, and visualization methods, the authors' approach has the benefits of universality and simplicity of data analysis, regardless of data types, structures, or specific field of science. First, mathematical preparation is described. The RMT-PCA and the RMT-test utilize the cross-correlation matrix of time series, C = XXT, where X represents a rectangular matrix of N rows and L columns and XT represents the transverse matrix of X. The RMT-PCA uses N samples of time series of length L. The RMT-test uses N elements of length L by cutting a single data to N pieces. Because C is symmetric, namely, C = CT, it can be converted to a diagonal matrix of eigenvalues by a similarity transformation SCST using an orthogonal matrix S. When N is significantly large, the histogram of the eigenvalue distribution can be compared to the theoretical formula derived in the context of the random matrix theory (RMT, in abbreviation). Then the RMT-PCA is applied to high-frequency stock prices in Japanese and American markets. This approach proves its effectiveness in extracting "trendy" business sectors of the financial market over the prescribed time scale. In this case, X consists of N stock- prices of length L, and the correlation matrix C is an N by N square matrix, whose element at the i-th row and j-th column is the inner product of the price time series of the length L of the i-th stock and the j-th stock of the equal length L. Next, the RMT-test is applied to measure randomness of various random number generators, including algorithmically generated random numbers and physically generated random numbers. The book concludes by demonstrating three applications of the RMT-test: (1) a comparison of hash functions, (2) choice of safe stocks, and (3) prediction of stock index by means of a sudden change of randomness.
Book Synopsis Smart Grid using Big Data Analytics by : Robert C. Qiu
Download or read book Smart Grid using Big Data Analytics written by Robert C. Qiu and published by John Wiley & Sons. This book was released on 2017-04-17 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.
Book Synopsis Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning by : Mohammad Shamsul Arefin
Download or read book Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning written by Mohammad Shamsul Arefin and published by Springer Nature. This book was released on with total page 1053 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Cognitive Networked Sensing and Big Data by : Robert Qiu
Download or read book Cognitive Networked Sensing and Big Data written by Robert Qiu and published by Springer Science & Business Media. This book was released on 2013-08-04 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless Distributed Computing and Cognitive Sensing defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The author will discuss the integration of software defined radio implementation and testbed development. The book will also bridge new research results and contextual reviews. Also the author provides an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed computing.
Book Synopsis Application of Big Data, Deep Learning, Machine Learning, and Other Advanced Analytical Techniques in Environmental Economics and Policy by : Tsun Se Cheong
Download or read book Application of Big Data, Deep Learning, Machine Learning, and Other Advanced Analytical Techniques in Environmental Economics and Policy written by Tsun Se Cheong and published by Frontiers Media SA. This book was released on 2022-07-25 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Inference and Machine Learning for Big Data by : Mayer Alvo
Download or read book Statistical Inference and Machine Learning for Big Data written by Mayer Alvo and published by Springer Nature. This book was released on 2022-11-30 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems. The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented. This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications.
Book Synopsis Smart Flow Control Processes in Micro Scale Volume 2 by : Bengt Sunden
Download or read book Smart Flow Control Processes in Micro Scale Volume 2 written by Bengt Sunden and published by MDPI. This book was released on 2020-12-29 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, microfluidic devices with a large surface-to-volume ratio have witnessed rapid development, allowing them to be successfully utilized in many engineering applications. A smart control process has been proposed for many years, while many new innovations and enabling technologies have been developed for smart flow control, especially concerning “smart flow control” at the microscale. This Special Issue aims to highlight the current research trends related to this topic, presenting a collection of 33 papers from leading scholars in this field. Among these include studies and demonstrations of flow characteristics in pumps or valves as well as dynamic performance in roiling mill systems or jet systems to the optimal design of special components in smart control systems.
Book Synopsis Big Data Analytics for Smart Urban Systems by : Saeid Pourroostaei Ardakani
Download or read book Big Data Analytics for Smart Urban Systems written by Saeid Pourroostaei Ardakani and published by Springer Nature. This book was released on 2023-10-29 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics for Smart Urban Systems aims to introduce Big data solutions for urban sustainability smart applications, particularly for smart urban systems. It focuses on intelligent big data which takes the benefits of machine learning to analyse large and rapidly changing datasets in smart urban systems. The state-of-the-art Big data analytics applications are presented and discussed to highlight the feasibility of big data and machine learning solutions to enhance smart urban systems, smart operations, urban management, and urban governance. The key benefits of this book are, (1) to introduce the principles of machine learning-enabled big data analysis in smart urban systems, (2) to present the state-of-the-art data analysis solutions in smart management and operations, and (3) to understand the principles of big data analytics for smart cities and communities. Endorsements ‘Over the many years of collaboration between academia and industry, we noticed the common language is ‘big data’; with that, we have developed novel ideas to bridge the gaps and help promote innovation, technologies, and science’.- Tian Tang, Independent Researcher, China ‘Big Data Analytics is a fascinating research area, particularly for cities and city transformations. This book is valuable to those who think vigorously and aim to act ahead’.- Li Xie, Independent Researcher, China ‘For urban critiques, knowledge trains aspiring opportunities toward outstanding manifestations. Smartness has evolved or/ advanced rambunctious & embracing realities along (with) novel directions and nurturing integrated city knowledge’.- Aaron Golden, SELECT Consultants, UK
Book Synopsis Principal Component Analysis by : I.T. Jolliffe
Download or read book Principal Component Analysis written by I.T. Jolliffe and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.
Book Synopsis Big Data in Omics and Imaging by : Momiao Xiong
Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2017-12-01 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.
Book Synopsis Principles and Practice of Big Data by : Jules J. Berman
Download or read book Principles and Practice of Big Data written by Jules J. Berman and published by Academic Press. This book was released on 2018-07-23 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines. Presents new methodologies that are widely applicable to just about any project involving large and complex datasets Offers readers informative new case studies across a range scientific and engineering disciplines Provides insights into semantics, identification, de-identification, vulnerabilities and regulatory/legal issues Utilizes a combination of pseudocode and very short snippets of Python code to show readers how they may develop their own projects without downloading or learning new software
Book Synopsis Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) by : Zehui Zhan
Download or read book Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) written by Zehui Zhan and published by Springer Nature. This book was released on 2023-01-20 with total page 1364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. The 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE2022) was held on April 8-10, 2022 in Beijing, China. ICBDIE2022 is to bring together innovative academics and industrial experts in the field of Big Data and Informatization Education to a common forum. The primary goal of the conference is to promote research and developmental activities in Big Data and Informatization Education and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in international conference on Big Data and Informatization Education and related areas.
Book Synopsis Big Data Analysis and Artificial Intelligence for Medical Sciences by : Bruno Carpentieri
Download or read book Big Data Analysis and Artificial Intelligence for Medical Sciences written by Bruno Carpentieri and published by John Wiley & Sons. This book was released on 2024-05-31 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analysis and Artificial Intelligence for Medical Sciences Overview of the current state of the art on the use of artificial intelligence in medicine and biology Big Data Analysis and Artificial Intelligence for Medical Sciences demonstrates the efforts made in the fields of Computational Biology and medical sciences to design and implement robust, accurate, and efficient computer algorithms for modeling the behavior of complex biological systems much faster than using traditional modeling approaches based solely on theory. With chapters written by international experts in the field of medical and biological research, Big Data Analysis and Artificial Intelligence for Medical Sciences includes information on: Studies conducted by the authors which are the result of years of interdisciplinary collaborations with clinicians, computer scientists, mathematicians, and engineers Differences between traditional computational approaches to data processing (those of mathematical biology) versus the experiment-data-theory-model-validation cycle Existing approaches to the use of big data in the healthcare industry, such as through IBM’s Watson Oncology, Microsoft’s Hanover, and Google’s DeepMind Difficulties in the field that have arisen as a result of technological changes, and potential future directions these changes may take A timely and up-to-date resource on the integration of artificial intelligence in medicine and biology, Big Data Analysis and Artificial Intelligence for Medical Sciences is of great benefit not only to professional scholars, but also MSc or PhD program students eager to explore advancement in the field.
Book Synopsis Big Data Analytics by : Arun K. Somani
Download or read book Big Data Analytics written by Arun K. Somani and published by CRC Press. This book was released on 2017-10-30 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.
Book Synopsis Digital Designs for Money, Markets, and Social Dilemmas by : Yuji Aruka
Download or read book Digital Designs for Money, Markets, and Social Dilemmas written by Yuji Aruka and published by Springer Nature. This book was released on 2022-05-23 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: An innovative feature of this book is its econocentric structure, focusing on digital designs. From the outset, econocentrism is assumed to be a core engine of capitalism, like money. The new coronavirus pandemic has changed lifestyles worldwide, which are unlikely ever to return in their original form. This great transformation will change the nature of the socio-economic system itself and will be centered on digital designs. At present, money already is beginning to undergo a major revolution in that sense. Many books dealing with digital designs and innovations have been published, but few if any of them focus on monetary and analytical methods in the way that this present volume does.The book then contains 6 parts: Evolution of money and thinking complexities in the AI era; Goods market and the future of labor market; Computational social approaches to social dilemmas, smart city, cryptocurrencies; Artificial market experiments; The randomness and high frequencies in financial data; Other trading strategy issues and the effects of AI usage. These issues may be indispensable subjects in our age. Study these subject, and have a step forward to the future society!
Book Synopsis New Insights on Principal Component Analysis by : Fausto Pedro García Márquez
Download or read book New Insights on Principal Component Analysis written by Fausto Pedro García Márquez and published by BoD – Books on Demand. This book was released on 2024-02-07 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book on Principal Component Analysis (PCA) extensively explores the core analyses and case studies within this field, incorporating the latest advancements. Each chapter delves into various disciplines like engineering, administration, economics, and technology, showcasing diverse applications and the utility of PCA. The book emphasizes the integration of PCA with other algorithms and methodologies, highlighting the enhancements achieved through combined approaches. Moreover, the book elucidates updated versions or iterations of PCA, detailing their descriptions and practical applications.
