Tensor Computation For Data Analysis

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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.

Advances In Artificial Intelligence Computation And Data Science

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Author : Tuan D. Pham
language : en
Publisher: Springer Nature
Release Date : 2021-07-12

Advances In Artificial Intelligence Computation And Data Science written by Tuan D. Pham and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-12 with Science categories.

Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.

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

Tensor Computation For Seismic Data Processing

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Author : FENG. PAN QIAN (SHENGLI. ZHANG, GULAN.)
language : en
Publisher:
Release Date : 2025-05-03

Tensor Computation For Seismic Data Processing written by FENG. PAN QIAN (SHENGLI. ZHANG, GULAN.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-03 with Science categories.

Research On Tensor Computation And Its Application On Data Science

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Author : Zequn Zheng
language : en
Publisher:
Release Date : 2023

Research On Tensor Computation And Its Application On Data Science written by Zequn Zheng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

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.

Algorithms In Data Mining Using Matrix And Tensor Methods

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Author : Berkant Savas
language : en
Publisher:
Release Date : 2008

Algorithms In Data Mining Using Matrix And Tensor Methods written by Berkant Savas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Algorithms categories.

Society Of Petroleum Engineers Journal

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Author : Society of Petroleum Engineers of AIME.
language : en
Publisher:
Release Date : 1964

Society Of Petroleum Engineers Journal written by Society of Petroleum Engineers of AIME. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1964 with Petroleum engineering categories.

Statistical Analysis Of The Rate Of Strain Tensor In Compressible Homogeneous Turbulence

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Author : Institute for Computer Applications in Science and Engineering
language : en
Publisher:
Release Date : 1992

Statistical Analysis Of The Rate Of Strain Tensor In Compressible Homogeneous Turbulence written by Institute for Computer Applications in Science and Engineering and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.

Journal Of The American Statistical Association

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Author :
language : en
Publisher:
Release Date : 2009

Journal Of The American Statistical Association written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Electronic journals categories.