
Advances in Independent Component Analysis and Learning Machines
- 1st Edition - April 15, 2015
- Imprint: Academic Press
- Editors: Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 8 0 2 8 0 6 - 3
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 2 8 0 7 - 0
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 it… Read more

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Request a sales quoteIn 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
University and industry researchers applying independent component analysis in the fields of pattern recognition, signal and image processing, medical imaging and telecommunications.
Part I: Methods
Chapter 1: The initial convergence rate of the FastICA algorithm: The “One-Third Rule”
- Abstract
- 1.1 Introduction
- 1.2 Statistical analysis of the FastICA algorithm
- 1.3 Stationary point analysis of the FastICA algorithm
- 1.4 Initial convergence of the FastICA algorithm for two-source mixtures
- 1.5 Initial convergence of the FastICA algorithm for three or more source mixtures
- 1.6 Numerical evaluations
- 1.7 Conclusion
- Appendix
- Acknowledgments
- Notes
Chapter 2: Improved variants of the FastICA algorithm
- Abstract
- 2.1 Introduction
- 2.2 Accuracy of One-Unit and Symmetric FastICA
- 2.3 Global Convergence
- 2.4 Approaching Cramér-Rao bound
- 2.5 FastICA in presence of additive noise
- Appendix: Generalized Gaussian Distributions
- Acknowledgments
- Notes
Chapter 3: A unified probabilistic model for independent and principal component analysis
- Abstract
- 3.1 Introduction
- 3.2 Variance of components as separate parameter
- 3.3 Analysis of maximum likelihood estimation
- 3.4 Conclusion
Chapter 4: Riemannian optimization in complex-valued ICA
- Abstract
- 4.1 Introduction
- 4.2 Overview of optimization under unitary matrix constraint
- 4.3 Geodesic method for optimizing under unitary constraint
- 4.4 Example on signal separation in MIMO system
- 4.5 Conclusion
Chapter 5: Nonadditive optimization
- Abstract
- 5.1 Introduction
- 5.2 Additive Optimization
- 5.3 Fast Fixed-Point Approximated Newton Algorithms
- 5.4 Fixed-point algorithms for kernel learning
- 5.5 Geodesic Updates in Stiefel Manifolds
- 5.6 Multiplicative updates
- 5.7 Discussion
- Notes
Chapter 6: Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning
- Abstract
- 6.1 A brief overview on denoising studies
- 6.2 LFA-BYY denoising method
- 6.3 BYY harmony learning algorithm for LFA
- 6.4 Experiments and discussion
- 6.5 Concluding remarks
Chapter 7: Unsupervised deep learning: A short review
- Abstract
- 7.1 Introduction
- 7.2 Multilayer Perceptron Networks
- 7.3 Deep learning
- 7.4 Restricted Boltzmann Machines
- 7.5 Deep Belief Networks
- 7.6 Deep Boltzmann Machines
- 7.7 Nonlinear Autoencoders
- 7.8 Neural Autoregressive Density Estimator (NADE)
- 7.9 Conclusions
Chapter 8: From neural PCA to deep unsupervised learning
- Abstract
- 8.1 Introduction
- 8.2 Ladder network: an autoencoder which can discard information
- 8.3 Parallel learning on every layer
- 8.4 Experiments
- 8.5 Discussion
- 8.6 Conclusions
- Acknowledgments
- Notes
Part II: Applications
Chapter 9: Two decades of local binary patterns: A survey
- Abstract
- 9.1 Introduction
- 9.2 An overview of basic LBP operators
- 9.3 LBP variants in the spatial domain
- 9.4 Spatiotemporal and other domains
- 9.5 Future challenges
- 9.6 Conclusions
Chapter 10: Subspace approach in spectral color science
- Abstract
- 10.1 Introduction
- 10.2 Principal component analysis
- 10.3 Independent component analysis
- 10.4 Comparison of methods
- 10.5 Spectral color applications
- 10.6 Conclusions
Chapter 11: From pattern recognition methods to machine vision applications
- Abstract
- 11.1 Introduction
- 11.2 From human vision to machine vision
- 11.3 Visual inspection and computational vision
- 11.4 Medical image processing and analysis
- 11.5 Biomolecular vision
- 11.6 Conclusions
- Acknowledgments
Chapter 12: Advances in visual concept detection: Ten years of TRECVID
- Abstract
- 12.1 Introduction
- 12.2 Parts of a video retrieval system
- 12.3 Concept detection in PicSOM
- 12.4 Experiments
- 12.5 Conclusions
Chapter 13: On the applicability of latent variable modeling to research system data
- Abstract
- 13.1 Introduction
- 13.2 Problem setting
- 13.3 Methods
- 13.4 Results
- 13.5 Discussion and further work
- Acknowledgment
- Notes
- Edition: 1
- Published: April 15, 2015
- Imprint: Academic Press
- No. of pages: 328
- Language: English
- Hardback ISBN: 9780128028063
- eBook ISBN: 9780128028070
EB
Ella Bingham
SK
Samuel Kaski
JL
Jorma Laaksonen
JL