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Tensor Computation for Data Analysis [electronic resource] / by Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu.

By: Contributor(s): Material type: TextTextLanguage: İngilizce Publisher: Cham : Springer International Publishing : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030743864
Subject(s): LOC classification:
  • TA347.T4
Online resources:
Contents:
1- Tensor Computation -- 2-Tensor Decomposition -- 3-Tensor Dictionary Learning -- 4-Low Rank Tensor Recovery -- 5-Coupled Tensor for Data Analysis -- 6-Robust Principal Tensor Component Analysis -- 7-Tensor Regression -- 8-Statistical Tensor Classification -- 9-Tensor Subspace Cluster -- 10-Tensor Decomposition in Deep Networks -- 11-Deep Networks for Tensor Approximation -- 12-Tensor-based Gaussian Graphical Model -- 13-Tensor Sketch. .
Summary: 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.
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Item type Current library Home library Collection Call number Copy number Status Date due Barcode
E-Book E-Book Merkez Kütüphane Merkez Kütüphane E-Kitap Koleksiyonu TA347.T4EBK (Browse shelf(Opens below)) 1 Geçerli değil-e-Kitap / Not applicable-e-Book EBK02761

1- Tensor Computation -- 2-Tensor Decomposition -- 3-Tensor Dictionary Learning -- 4-Low Rank Tensor Recovery -- 5-Coupled Tensor for Data Analysis -- 6-Robust Principal Tensor Component Analysis -- 7-Tensor Regression -- 8-Statistical Tensor Classification -- 9-Tensor Subspace Cluster -- 10-Tensor Decomposition in Deep Networks -- 11-Deep Networks for Tensor Approximation -- 12-Tensor-based Gaussian Graphical Model -- 13-Tensor Sketch. .

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.

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