Bayesian Tensor Decomposition For Signal Processing And Machine Learning

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Bayesian Tensor Decomposition for Signal Processing and Machine Learning

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Author : Lei Cheng
Publisher : Springer Nature
ISBN 13 : 3031224388
Total Pages : 189 pages
Book Rating : 4.0/5 (312 download)

Book Synopsis Bayesian Tensor Decomposition for Signal Processing and Machine Learning by : Lei Cheng

Download or read book Bayesian Tensor Decomposition for Signal Processing and Machine Learning written by Lei Cheng and published by Springer Nature. This book was released on 2023-02-16 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including blind source separation; social network mining; image and video processing; array signal processing; and, wireless communications. The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed. Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.

Tensor Computation for Data Analysis

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Author : Yipeng Liu
Publisher : Springer Nature
ISBN 13 : 3030743861
Total Pages : 347 pages
Book Rating : 4.0/5 (37 download)

Book Synopsis Tensor Computation for Data Analysis by : Yipeng Liu

Download or read book Tensor Computation for Data Analysis written by Yipeng Liu and published by Springer Nature. This book was released on 2021-08-31 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Tensors for Data Processing

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Author : Yipeng Liu
Publisher : Academic Press
ISBN 13 : 0323859658
Total Pages : 598 pages
Book Rating : 4.3/5 (238 download)

Book Synopsis Tensors for Data Processing by : Yipeng Liu

Download or read book Tensors for Data Processing written by Yipeng Liu and published by Academic Press. This book was released on 2021-10-21 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Signal Processing and Machine Learning Theory

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Author : Paulo S.R. Diniz
Publisher : Elsevier
ISBN 13 : 032397225X
Total Pages : 1236 pages
Book Rating : 4.3/5 (239 download)

Book Synopsis Signal Processing and Machine Learning Theory by : Paulo S.R. Diniz

Download or read book Signal Processing and Machine Learning Theory written by Paulo S.R. Diniz and published by Elsevier. This book was released on 2023-07-10 with total page 1236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Source Separation and Machine Learning

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Author : Jen-Tzung Chien
Publisher : Academic Press
ISBN 13 : 0128045779
Total Pages : 386 pages
Book Rating : 4.1/5 (28 download)

Book Synopsis Source Separation and Machine Learning by : Jen-Tzung Chien

Download or read book Source Separation and Machine Learning written by Jen-Tzung Chien and published by Academic Press. This book was released on 2018-10-16 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems

Neural Information Processing

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Author : Biao Luo
Publisher : Springer Nature
ISBN 13 : 9819980798
Total Pages : 614 pages
Book Rating : 4.8/5 (199 download)

Book Synopsis Neural Information Processing by : Biao Luo

Download or read book Neural Information Processing written by Biao Luo and published by Springer Nature. This book was released on 2023-11-13 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields.

Nonnegative Matrix and Tensor Factorizations

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Author : Andrzej Cichocki
Publisher : John Wiley & Sons
ISBN 13 : 9780470747285
Total Pages : 500 pages
Book Rating : 4.7/5 (472 download)

Book Synopsis Nonnegative Matrix and Tensor Factorizations by : Andrzej Cichocki

Download or read book Nonnegative Matrix and Tensor Factorizations written by Andrzej Cichocki and published by John Wiley & Sons. This book was released on 2009-07-10 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Research on Tensor Computation and Its Application on Data Science

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Author : Zequn Zheng
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)

Book Synopsis Research on Tensor Computation and Its Application on Data Science by : Zequn Zheng

Download or read book Research on Tensor Computation and Its Application on Data Science written by Zequn Zheng and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tensors or multidimensional arrays are higher order generalizations of matrices. They are natural structures for expressing data that have inherent higher order structures. Tensor decompositions and Tensor approximations play an important role in learning those hidden structures. They have many applications in machine learning, statistical learning, data science, signal processing, neuroscience, and more. Canonical Polyadic Decomposition (CPD) is a tensor decomposition that decomposes a tensor to minimal number of summation of rank 1 tensors. While for a given tensor, Low-Rank Tensor Approximation (LRTA) aims at finding a new one whose rank is small and that is close to the given one. We study the generating polynomials for computing tensor decompositions and low-rank approximations for given tensors and propose methods that can compute tensor decompositions for generic tensors under certain rank conditions. For low-rank tensor approximation, the proposed method guarantees that the constructed tensor is a good enough low-rank approximation if the tensor is to be approximated is close enough to a low-rank one. The proof built on perturbation analysis is presented. When the rank is higher than the second dimension, we are not able to find the common zeros of generating polynomials directly. In this case, we need to use the quadratic equations that we get from those generating polynomials. We show that under certain conditions, we are able to find the tensor decompositions using standard linear algebra operations (i.e., solving linear systems, singular value decompositions, QR decompositions). Numerical examples and some comparisons are presented to show the performance of our algorithm. Multi-view learning is frequently used in data science. The pairwise correlation maximization is a classical approach for exploring the consensus of multiple views. Since the pairwise correlation is inherent for two views, the extensions to more views can be diversified and the intrinsic interconnections among views are generally lost. To address this issue, we propose to maximize the high-order tensor correlation. This can be formulated as a low-rank approximation problem with the high-order correlation tensor of multi-view data. We propose to use the generating polynomial method to efficiently solve the high-order correlation maximization problem of tensor canonical correlation analysis for multi-view learning. Numerical results on simulated data and two real multi-view data sets demonstrate that our proposed method not only consistently outperforms existing methods but also is efficient for large scale tensors.

Recent Applications in Data Clustering

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Author : Harun Pirim
Publisher : BoD – Books on Demand
ISBN 13 : 178923526X
Total Pages : 250 pages
Book Rating : 4.7/5 (892 download)

Book Synopsis Recent Applications in Data Clustering by : Harun Pirim

Download or read book Recent Applications in Data Clustering written by Harun Pirim and published by BoD – Books on Demand. This book was released on 2018-08-01 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering has emerged as one of the more fertile fields within data analytics, widely adopted by companies, research institutions, and educational entities as a tool to describe similar/different groups. The book Recent Applications in Data Clustering aims to provide an outlook of recent contributions to the vast clustering literature that offers useful insights within the context of modern applications for professionals, academics, and students. The book spans the domains of clustering in image analysis, lexical analysis of texts, replacement of missing values in data, temporal clustering in smart cities, comparison of artificial neural network variations, graph theoretical approaches, spectral clustering, multiview clustering, and model-based clustering in an R package. Applications of image, text, face recognition, speech (synthetic and simulated), and smart city datasets are presented.

Computational Statistics in Data Science

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Author : Richard A. Levine
Publisher : John Wiley & Sons
ISBN 13 : 1119561086
Total Pages : 672 pages
Book Rating : 4.1/5 (195 download)

Book Synopsis Computational Statistics in Data Science by : Richard A. Levine

Download or read book Computational Statistics in Data Science written by Richard A. Levine and published by John Wiley & Sons. This book was released on 2022-03-23 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.

Neural Information Processing

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Author : Haiqin Yang
Publisher : Springer Nature
ISBN 13 : 3030638308
Total Pages : 834 pages
Book Rating : 4.0/5 (36 download)

Book Synopsis Neural Information Processing by : Haiqin Yang

Download or read book Neural Information Processing written by Haiqin Yang and published by Springer Nature. This book was released on 2020-11-18 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually.The 187 full papers presented were carefully reviewed and selected from 618 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 12532, is organized in topical sections on human-computer interaction; image processing and computer vision; natural language processing.

Bayesian Speech and Language Processing

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Author : Shinji Watanabe
Publisher : Cambridge University Press
ISBN 13 : 1316352102
Total Pages : 447 pages
Book Rating : 4.3/5 (163 download)

Book Synopsis Bayesian Speech and Language Processing by : Shinji Watanabe

Download or read book Bayesian Speech and Language Processing written by Shinji Watanabe and published by Cambridge University Press. This book was released on 2015-07-15 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing.

Algorithmic Aspects of Machine Learning

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Author : Ankur Moitra
Publisher : Cambridge University Press
ISBN 13 : 1107184584
Total Pages : 161 pages
Book Rating : 4.1/5 (71 download)

Book Synopsis Algorithmic Aspects of Machine Learning by : Ankur Moitra

Download or read book Algorithmic Aspects of Machine Learning written by Ankur Moitra and published by Cambridge University Press. This book was released on 2018-09-27 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.

Machine Learning and Knowledge Discovery in Databases

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Author : Annalisa Appice
Publisher : Springer
ISBN 13 : 3319235257
Total Pages : 802 pages
Book Rating : 4.3/5 (192 download)

Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Annalisa Appice

Download or read book Machine Learning and Knowledge Discovery in Databases written by Annalisa Appice and published by Springer. This book was released on 2015-08-28 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

Artificial Intelligence in Healthcare and Medicine

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Author : Kayvan Najarian
Publisher : CRC Press
ISBN 13 : 1000565815
Total Pages : 300 pages
Book Rating : 4.0/5 (5 download)

Book Synopsis Artificial Intelligence in Healthcare and Medicine by : Kayvan Najarian

Download or read book Artificial Intelligence in Healthcare and Medicine written by Kayvan Najarian and published by CRC Press. This book was released on 2022-04-06 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor.

Smart Water Grids

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Author : Panagiotis Tsakalides
Publisher : CRC Press
ISBN 13 : 1351986163
Total Pages : 323 pages
Book Rating : 4.3/5 (519 download)

Book Synopsis Smart Water Grids by : Panagiotis Tsakalides

Download or read book Smart Water Grids written by Panagiotis Tsakalides and published by CRC Press. This book was released on 2018-04-17 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: The effects of climate change, rapid urbanization, and aging infrastructure challenge water policymakers to confront a radical paradigm shift in water resources utilization. Recent advances in sensing, networking, processing, and control have provided the means for sustainable solutions in water management, and their implementation in water infrastructures is collectively referred to as "smart water grids." Smart water grids depend upon cyber-physical system principles to effectively respond to issues regarding the scalability and reliability of dynamic and inaccessible environments. As such, unique smart water grid issues associated with front-end signal processing, communication, control, and data analysis must be jointly addressed, while sophisticated techniques for data analytics must be introduced into cyber-physical systems research. This book provides a thorough description of the best practices for designing and implementing cyber-physical systems that are tailored to different aspects of smart water grids. It is organized into three distinct, yet complementary areas, namely: the theory behind water-oriented cyber-physical systems with an emphasis on front-end sensing and processing, communication technologies, and learning techniques over water data; the applications and emerging topics of cyber-physical systems for water urban infrastructures, including real-life deployments, modern control tools, and economic aspects for smart water grids; and the applications and emerging topics across natural environments, emphasizing the evolution of fresh water resources. The structured discussion yields a rich, comprehensive body of knowledge on this emerging topic of research and engineering. As water issues intensify on a global scale, this book offers an algorithmic and practical toolkit for intermediate and advanced readers as well as professionals and researchers who are active in, or interested in, learning more about smart water grids. Key Features: Emphasizes the multidisciplinary nature of this emerging topic, covering both theoretical and practical aspects of this area while providing insights on existing deployments, which can serve as design examples for new applications. Explores how modern signal processing and machine learning techniques can contribute and enrich the potential of smart water grids, well beyond conventional closed-loop control techniques. Highlights complementary aspects that will help shape the future of smart water grids, such as consumption awareness, economic aspects, and control tools in industrial water treatment as well as the impact of climate change on fresh water resources. Enables the reader to better understand this emerging topic, investing in current state-of-the-art and future technological roadmaps for smart water grids.

Tensor Decompositions and Algorithms for Efficient Multidimensional Signal Processing

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Author : Liana Khamidullina
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

Book Synopsis Tensor Decompositions and Algorithms for Efficient Multidimensional Signal Processing by : Liana Khamidullina

Download or read book Tensor Decompositions and Algorithms for Efficient Multidimensional Signal Processing written by Liana Khamidullina and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: