Tensor Regression
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Tensor Regression
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Author : Jiani Liu
language : en
Publisher:
Release Date : 2021-09-27
Tensor Regression written by Jiani Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-27 with categories.
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.
Tensor Computation For Data Analysis
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Author : Yipeng Liu
language : en
Publisher: Springer Nature
Release Date : 2021-08-31
Tensor Computation For Data Analysis written by Yipeng Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Technology & Engineering categories.
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.
High Performance Tensor Computations In Scientific Computing And Data Science
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Author : Edoardo Angelo Di Napoli
language : en
Publisher: Frontiers Media SA
Release Date : 2022-11-08
High Performance Tensor Computations In Scientific Computing And Data Science written by Edoardo Angelo Di Napoli and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-08 with Science categories.
Tensors For Data Processing
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Author : Yipeng Liu
language : en
Publisher: Academic Press
Release Date : 2021-10-21
Tensors For Data Processing written by Yipeng Liu and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-21 with Technology & Engineering categories.
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
Multimodal And Tensor Data Analytics For Industrial Systems Improvement
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Author : Nathan Gaw
language : en
Publisher: Springer Nature
Release Date : 2024-05-16
Multimodal And Tensor Data Analytics For Industrial Systems Improvement written by Nathan Gaw and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Mathematics categories.
This volume covers the latest methodologies for using multimodal data fusion and analytics across several applications. The curated content presents recent developments and challenges in multimodal data analytics and shines a light on a pathway toward new research developments. Chapters are composed by eminent researchers and practitioners who present their research results and ideas based on their expertise. As data collection instruments have improved in quality and quantity for many applications, there has been an unprecedented increase in the availability of data from multiple sources, known as modalities. Modalities express a large degree of heterogeneity in their form, scale, resolution, and accuracy. Determining how to optimally combine the data for prediction and characterization is becoming increasingly important. Several research studies have investigated integrating multimodality data and discussed the challenges and limitations of multimodal data fusion. This volume provides a topical overview of various methods in multimodal data fusion for industrial engineering and operations research applications, such as manufacturing and healthcare. Advancements in sensing technologies and the shift toward the Internet of Things (IoT) has transformed and will continue to transform data analytics by producing new requirements and more complex forms of data. The abundance of data creates an unprecedented opportunity to design more efficient systems and make near-optimal operational decisions. On the other hand, the structural complexity and heterogeneity of the generated data pose a significant challenge to extracting useful features and patterns for making use of the data and facilitating decision-making. Therefore, continual research is needed to develop new statistical and analytical methodologies that overcome these data challenges and turn them into opportunities.
Tensor Structures And Applications
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-20
Tensor Structures And Applications written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-20 with Computers categories.
"Tensor Structures and Applications" "Tensor Structures and Applications" offers a comprehensive and rigorous exploration of tensors, guiding the reader from foundational mathematics to advanced, real-world applications. The book opens with a thorough treatment of the mathematical underpinnings, framing tensors as a natural extension of vectors and matrices through multilinear algebra, formal classification by order and type, and detailed conventions for representation and notation. Further foundational chapters bridge these ideas into the realm of differential geometry, introducing the calculus of tensor fields on manifolds, metric tensors, and their indispensable role in Riemannian geometry and general relativity. Building on these theoretical structures, the text presents an in-depth treatment of tensor decompositions, factorization strategies, and the algorithms that power high-dimensional data analysis. Special attention is given to the unique computational challenges posed by tensor methods, covering efficient data storage, parallel and distributed computing, automatic differentiation, and best practices in leading software libraries. These chapters serve both as a roadmap and a toolbox for researchers and practitioners working with complex, high-order data in numerical and machine learning contexts. The latter sections of the book survey an impressive array of contemporary applications, from signal processing and computer vision—where tensors enable sophisticated tasks such as multi-modal filtering and high-dimensional image analysis—to scientific computing, continuum mechanics, quantum information, and engineering simulations. "Tensor Structures and Applications" concludes by charting emerging research directions, including geometric deep learning, topological data analysis, and multiway network modeling, providing readers with both a solid foundation and a forward-looking perspective in this rapidly evolving field.
Deep Learning For Computational Problems In Hardware Security
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Author : Pranesh Santikellur
language : en
Publisher: Springer Nature
Release Date : 2022-09-15
Deep Learning For Computational Problems In Hardware Security written by Pranesh Santikellur and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-15 with Technology & Engineering categories.
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.
Mathematical And Statistical Methods For Actuarial Sciences And Finance
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Author : Marco Corazza
language : en
Publisher: Springer
Release Date : 2018-07-17
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Marco Corazza and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-17 with Business & Economics categories.
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.
New Approaches For Multidimensional Signal Processing
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Author : Lakhmi C. Jain
language : en
Publisher: Springer Nature
Release Date : 2025-05-26
New Approaches For Multidimensional Signal Processing written by Lakhmi C. Jain and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-26 with Computers categories.
This book is a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2024), held at Technical University of Sofia, Sofia, Bulgaria, during 25–27 July 2024. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional (MD) image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, MD multimedia systems, data-based MD image retrieval and knowledge data mining, jamming image recognition and surface defects segmentation, MD signal analysis aimed at medical decision support, MD image processing in robot systems, 3D and multi-view visualization in environmental art, VR and reinforcement learning applications, tensor-based mip-map implementation, recursive filtration of MD images, and many more.
Proceedings Of The Future Technologies Conference Ftc 2022 Volume 3
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Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2022-10-13
Proceedings Of The Future Technologies Conference Ftc 2022 Volume 3 written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-13 with Technology & Engineering categories.
The seventh Future Technologies Conference 2022 was organized in a hybrid mode. It received a total of 511 submissions from learned scholars, academicians, engineers, scientists and students across many countries. The papers included the wide arena of studies like Computing, Artificial Intelligence, Machine Vision, Ambient Intelligence and Security and their jaw- breaking application to the real world. After a double-blind peer review process 177 submissions have been selected to be included in these proceedings. One of the prominent contributions of this conference is the confluence of distinguished researchers who not only enthralled us by their priceless studies but also paved way for future area of research. The papers provide amicable solutions to many vexing problems across diverse fields. They also are a window to the future world which is completely governed by technology and its multiple applications. We hope that the readers find this volume interesting and inspiring and render their enthusiastic support towards it.