Advances in independent component analysis and learning machines
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 algorithmUn…
Read More ↓Title Statements
- Full title :Advances in independent component analysis and learning machines / edited by Ella Bingham [and three others].
Summary
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 algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t.
Read More ↓Edition Statements
- Publication Date:©2015
Publication Statements
- Publisher(s):Academic Press
- Place of Publication:Amsterdam, [Netherlands]
- Language:eng
- Publication Date:©2015
The Physical Object
- Description:1 online resource (329 pages) : : illustrations
Edition Identifiers
- ISBN:9780128028070, 0128028076
- oclc:on1229061711
- LC Classification:QA278
- Dewey Decimal:519.535
Table of Contents
<P>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</p>
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