Advances In Independent Component Analysis And Learning Machines by Ella BinghamAdvances In Independent Component Analysis And Learning Machines by Ella Bingham

Advances In Independent Component Analysis And Learning Machines

byElla BinghamEditorSamuel Kaski, Jorma Laaksonen

Hardcover | April 23, 2015

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In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.

Examples of topics which have developed from the advances of ICA, which are covered in the book are:

  • A unifying probabilistic model for PCA and ICA
  • Optimization methods for matrix decompositions
  • Insights into the FastICA algorithm
  • Unsupervised deep learning
  • Machine vision and image retrieval

  • A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning.
  • A diverse set of application fields, ranging from machine vision to science policy data.
  • Contributions from leading researchers in the field.
Ella Bingham received her Doctor of Science (PhD) degree in Computer Science in 2003, and MSc degree in Systems and Operations Research in 1998, both at Helsinki University of Technology. Her main research field has been statistical data analysis. She works at Helsinki Institute for Information Technology HIIT at Aalto University and U...
Title:Advances In Independent Component Analysis And Learning MachinesFormat:HardcoverDimensions:328 pages, 9.41 × 7.24 × 0.98 inPublished:April 23, 2015Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128028068

ISBN - 13:9780128028063


Table of Contents

Prologue: Sketching a Scholar Part 1: Methods Chapter 1. The Initial Convergence Rate of the FastICA Algorithm: The "One-Third Rule" Chapter 2. Improved variants of the FastICA algorithm Chapter 3. A unified probabilistic model for independent and principal component analysis Chapter 4. Riemannian optimization in complex-valued ICA Chapter 5. Non-Additive Optimization Chapter 6. Image denoising via local factor analysis under Bayesian Ying-Yang principle Chapter 7. Unsupervised Deep Learning: A Short Review Chapter 8. From Neural PCA to Deep Unsupervised Learning Part 2: Applications Chapter 9. Two Decades of Local Binary Patterns A Survey Chapter 10. Subspace approach in Spectral Color Science Chapter 11. From pattern recognition methods to machine vision applications Chapter 12. Advances in Visual Concept Detection: Ten Years of TRECVID Chapter 13. On the applicability of latent variable modeling to research system data