Tensor Regression

Author : Yipeng Liu,Jiani Liu,Zhen Long,Ce Zhu
Publisher : Springer Nature
Page : 347 pages
File Size : 44,5 Mb
Release : 2021-08-31
Category : Technology & Engineering
ISBN : 9783030743864

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Tensor Computation for Data Analysis by Yipeng Liu,Jiani Liu,Zhen Long,Ce Zhu Pdf

Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.

Author : Jiani Liu,Ce Zhu,Zhen Long,Yipeng Liu
Publisher : Unknown
Page : 128 pages
File Size : 45,7 Mb
Release : 2021-09-27
Category : Electronic
ISBN : 1680838865

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Tensor Regression by Jiani Liu,Ce Zhu,Zhen Long,Yipeng Liu Pdf

Tensor Regression is the first thorough overview of the fundamentals, motivations, popular algorithms, strategies for efficient implementation, related applications, available datasets, and software resources for tensor-based regression analysis.

Author : Nathan Gaw
Publisher : Springer Nature
Page : 388 pages
File Size : 54,7 Mb
Release : 2024-06-30
Category : Electronic
ISBN : 9783031530920

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Multimodal and Tensor Data Analytics for Industrial Systems Improvement by Nathan Gaw Pdf

Author : Derek Scott Young
Publisher : CRC Press
Page : 507 pages
File Size : 50,9 Mb
Release : 2018-10-03
Category : Mathematics
ISBN : 9781351650748

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Handbook of Regression Methods by Derek Scott Young Pdf

Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are applied. The organization of the handbook benefits both practitioners and researchers, who seek either to obtain a quick understanding of regression methods for specialized problems or to expand their own breadth of knowledge of regression topics. This handbook covers classic material about simple linear regression and multiple linear regression, including assumptions, effective visualizations, and inference procedures. It presents an overview of advanced diagnostic tests, remedial strategies, and model selection procedures. Finally, many chapters are devoted to a diverse range of topics, including censored regression, nonlinear regression, generalized linear models, and semiparametric regression. Features Presents a concise overview of a wide range of regression topics not usually covered in a single text Includes over 80 examples using nearly 70 real datasets, with results obtained using R Offers a Shiny app containing all examples, thus allowing access to the source code and the ability to interact with the analyses

Author : Pranesh Santikellur,Rajat Subhra Chakraborty
Publisher : Springer Nature
Page : 92 pages
File Size : 51,8 Mb
Release : 2022-09-15
Category : Technology & Engineering
ISBN : 9789811940170

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Deep Learning for Computational Problems in Hardware Security by Pranesh Santikellur,Rajat Subhra Chakraborty Pdf

The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book.

Author : Adedeji B. Badiru,Frank W. Ciarallo,Eric G. Mbonimpa
Publisher : CRC Press
Page : 609 pages
File Size : 44,6 Mb
Release : 2022-12-20
Category : Technology & Engineering
ISBN : 9781000798555

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Handbook of Scholarly Publications from the Air Force Institute of Technology (AFIT), Volume 1, 2000-2020 by Adedeji B. Badiru,Frank W. Ciarallo,Eric G. Mbonimpa Pdf

This handbook represents a collection of previously published technical journal articles of the highest caliber originating from the Air Force Institute of Technology (AFIT). The collection will help promote and affirm the leading-edge technical publications that have emanated from AFIT, for the first time presented as a cohesive collection. In its over 100 years of existence, AFIT has produced the best technical minds for national defense and has contributed to the advancement of science and technology through technology transfer throughout the nation. This handbook fills the need to share the outputs of AFIT that can guide further advancement of technical areas that include cutting-edge technologies such as blockchain, machine learning, additive manufacturing, 5G technology, navigational tools, advanced materials, energy efficiency, predictive maintenance, the internet of things, data analytics, systems of systems, modeling & simulation, aerospace product development, virtual reality, resource optimization, and operations management. There is a limitless vector to how AFIT’s technical contributions can impact the society. Handbook of Scholarly Publications from the Air Force Institute of Technology (AFIT), Volume 1, 2000-2020, is a great reference for students, teachers, researchers, consultants, and practitioners in broad spheres of engineering, business, industry, academia, the military, and government.

Author : Edoardo Angelo Di Napoli,Paolo Bientinesi,Jiajia Li,André Uschmajew
Publisher : Frontiers Media SA
Page : 192 pages
File Size : 50,5 Mb
Release : 2022-11-08
Category : Science
ISBN : 9782832504253

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High-Performance Tensor Computations in Scientific Computing and Data Science by Edoardo Angelo Di Napoli,Paolo Bientinesi,Jiajia Li,André Uschmajew Pdf

Author : Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira
Publisher : Springer Nature
Page : 679 pages
File Size : 55,7 Mb
Release : 2020-06-19
Category : Computers
ISBN : 9783030504335

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Computational Science – ICCS 2020 by Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira Pdf

The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.

Author : Paulo S.R. Diniz
Publisher : Elsevier
Page : 1236 pages
File Size : 48,6 Mb
Release : 2023-07-10
Category : Technology & Engineering
ISBN : 9780323972253

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Signal Processing and Machine Learning Theory by Paulo S.R. Diniz Pdf

Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc. Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools Presents core principles in signal processing theory and shows their applications Discusses some emerging signal processing tools applied in machine learning methods References content on core principles, technologies, algorithms and applications Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge

Author : Marco Corazza,María Durbán,Aurea Grané,Cira Perna,Marilena Sibillo
Publisher : Springer
Page : 518 pages
File Size : 44,7 Mb
Release : 2018-07-17
Category : Business & Economics
ISBN : 9783319898247

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Mathematical and Statistical Methods for Actuarial Sciences and Finance by Marco Corazza,María Durbán,Aurea Grané,Cira Perna,Marilena Sibillo Pdf

The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and is also of interest to other readers with quantitative background knowledge.

Author : Yipeng Liu
Publisher : Academic Press
Page : 598 pages
File Size : 51,5 Mb
Release : 2021-10-21
Category : Technology & Engineering
ISBN : 9780323859653

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Tensors for Data Processing by Yipeng Liu Pdf

Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing. This reference is ideal for students, researchers and industry developers who want to understand and use tensor-based data processing theories and methods. As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear data structure loss that occurs in classical matrix-based data processing methods. This move from matrix to tensors is beneficial for many diverse application areas, including signal processing, computer science, acoustics, neuroscience, communication, medical engineering, seismology, psychometric, chemometrics, biometric, quantum physics and quantum chemistry. Provides a complete reference on classical and state-of-the-art tensor-based methods for data processing Includes a wide range of applications from different disciplines Gives guidance for their application

Author : Yasuo Yoshioka,Jun Itami,Masahiko Oguchi,Takashi Nakano
Publisher : Springer
Page : 304 pages
File Size : 52,8 Mb
Release : 2018-08-01
Category : Medical
ISBN : 9789811304903

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Brachytherapy by Yasuo Yoshioka,Jun Itami,Masahiko Oguchi,Takashi Nakano Pdf

This book provides a comprehensive and up-to-date overview of major technical advances and research findings in the field of brachytherapy. Especially research conducted on brachytherapy in Japan has made tremendous strides, offering readers valuable insights into new treatment outcomes, evidence and techniques. In the respective chapters, each author elucidates cutting-edge findings, addressing both the basic and clinical aspects including the application to various cancers, and especially focusing on highly valuable data gleaned in Japan. As such, the book will benefit not only radiation oncologists but also basic researchers, radiation technologists, medical physicists and members of medical staff who are involved in the development and utilization of this advanced therapy.

Author : Jian Yang,Fang Fang,Changyin Sun
Publisher : Springer
Page : 895 pages
File Size : 52,7 Mb
Release : 2013-02-11
Category : Computers
ISBN : 9783642366697

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Intelligent Science and Intelligent Data Engineering by Jian Yang,Fang Fang,Changyin Sun Pdf

This book constitutes the proceedings of the third Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering, IScIDE 2012, held in Nanjing, China, in October 2012. The 105 papers presented were carefully peer-reviewed and selected from 429 submissions. Topics covered include pattern recognition; computer vision and image processing; machine learning and computational intelligence; knowledge discovery, data mining, and web mining; graphics and computer visualization; and multimedia processing and applications.

Author : Jong-Il Park,Junmo Kim
Publisher : Springer
Page : 639 pages
File Size : 44,7 Mb
Release : 2013-03-27
Category : Computers
ISBN : 9783642374845

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Computer Vision - ACCV 2012 Workshops by Jong-Il Park,Junmo Kim Pdf

The two volume set, consisting of LNCS 7728 and 7729, contains the carefully reviewed and selected papers presented at the nine workshops that were held in conjunction with the 11th Asian Conference on Computer Vision, ACCV 2012, in Daejeon, South Korea, in November 2012. From a total of 310 papers submitted, 78 were selected for presentation. LNCS 7728 contains the papers selected for the International Workshop on Computer Vision with Local Binary Pattern Variants, the Workshop on Computational Photography and Low-Level Vision, the Workshop on Developer-Centered Computer Vision, and the Workshop on Background Models Challenge. LNCS 7729 contains the papers selected for the Workshop on e-Heritage, the Workshop on Color Depth Fusion in Computer Vision, the Workshop on Face Analysis, the Workshop on Detection and Tracking in Challenging Environments, and the International Workshop on Intelligent Mobile Vision.

Author : Ivo D. Dinov,Milen Velchev Velev
Publisher : Walter de Gruyter GmbH & Co KG
Page : 489 pages
File Size : 43,8 Mb
Release : 2021-12-06
Category : Computers
ISBN : 9783110697827

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Data Science by Ivo D. Dinov,Milen Velchev Velev Pdf

The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.