rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Tensor Computation for Data Analysis
  name:
    name part: Liu, Yipeng.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Liu, Jiani.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Long, Zhen.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Zhu, Ce.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XX, 338 pages 132 illustrations, 119 illustrations in color.)
  abstract: 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, et cetera 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 forecast…
  table of 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. .
  note: by Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu.
  subject:
    topic: Electronic circuits
  subject:
    topic: Signal processing
  subject:
    topic: Image processing
  subject:
    topic: Speech processing systems
  subject:
    topic: Computer engineering
  subject:
    topic: Internet of things
  subject:
    topic: Embedded computer systems
  subject:
    topic: Circuits and Systems
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Cyber-physical systems, IoT
  classification: TK7888.4
  classification: 621.3815
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030743864
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 210831
    record change date: 20240805193309.6
    record identifier: in00004385474