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  • © 2000

Advances in Independent Component Analysis

Editors:

  • A state-of-the-art overview with contributions from the most respected and innovative researchers in the field

  • Contains significantly more advanced, novel and up-to-date theory than any other volume available

Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

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eBook USD 169.00
Price excludes VAT (USA)
  • ISBN: 978-1-4471-0443-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
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  • Tax calculation will be finalised during checkout
Softcover Book USD 219.99
Price excludes VAT (USA)

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Table of contents (14 chapters)

  1. Front Matter

    Pages I-XIX
  2. Temporal ICA Models

    1. Front Matter

      Pages 1-1
    2. Hidden Markov Independent Component Analysis

      • William D. Penny, Richard M. Everson, Stephen J. Roberts
      Pages 3-22
    3. Particle Filters for Non-Stationary ICA

      • Richard M. Everson, Stephen J. Roberts
      Pages 23-41
  3. The Validity of the Independence Assumption

    1. Front Matter

      Pages 43-43
    2. The Independence Assumption: Analyzing the Independence of the Components by Topography

      • Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki
      Pages 45-62
  4. Ensemble Learning and Applications

    1. Front Matter

      Pages 73-73
    2. Ensemble Learning

      • Harri Lappalainen, James W. Miskin
      Pages 75-92
    3. Ensemble Learning for Blind Image Separation and Deconvolution

      • James Miskin, David J. C. MacKay
      Pages 123-141
  5. Data Analysis and Applications

    1. Front Matter

      Pages 143-143
    2. Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions

      • Francesco Palmieri, Alessandra Budillon
      Pages 145-160
    3. Blind Separation of Noisy Image Mixtures

      • Lars Kai Hansen
      Pages 161-181
    4. Searching for Independence in Electromagnetic Brain Waves

      • Ricardo Vigário, Jaakko Särelä, Erkki Oja
      Pages 183-199
    5. Analysis of Optical Imaging Data Using Weak Models and ICA

      • John Porrill, James V. Stone, Jason Berwick, John Mayhew, Peter Coffey
      Pages 217-233
    6. Independent Components in Text

      • Thomas Kolenda, Lars Kai Hansen, Sigurdur Sigurdsson
      Pages 235-256
    7. Seeking Independence Using Biologically-Inspired ANN’s

      • Pei Ling Lai, Darryl Charles, Colin Fyfe
      Pages 257-276
  6. Back Matter

    Pages 277-279

About this book

Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

Keywords

  • Ensembl
  • artificial intelligence
  • artificial neural network
  • data analysis
  • electrical engineering
  • image processing
  • learning
  • modeling

Editors and Affiliations

  • Department of Computing and Information Systems, University of Paisley, Paisley, UK

    Mark Girolami

Bibliographic Information

Buying options

eBook USD 169.00
Price excludes VAT (USA)
  • ISBN: 978-1-4471-0443-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 219.99
Price excludes VAT (USA)