Advances in Independent Component Analysis
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 us...
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| Format: | eBook |
| Language: | English |
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London
Springer London
2000, 2000
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| Edition: | 1st ed. 2000 |
| Series: | Perspectives in Neural Computing
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- I Temporal ICA Models
- 1 Hidden Markov Independent Component Analysis
- 2 Particle Filters for Non-Stationary ICA
- II The Validity of the Independence Assumption
- 3 The Independence Assumption: Analyzing the Independence of the Components by Topography
- 4 The Independence Assumption: Dependent Component Analysis
- III Ensemble Learning and Applications
- 5 Ensemble Learning
- 6 Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons
- 7 Ensemble Learning for Blind Image Separation and Deconvolution
- IV Data Analysis and Applications
- 8 Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions
- 9 Blind Separation of Noisy Image Mixtures
- 10 Searching for Independence in Electromagnetic Brain Waves
- 11 ICA on Noisy Data: A Factor Analysis Approach
- 12 Analysis of Optical Imaging Data Using Weak Models and ICA
- 13 Independent Components in Text
- 14 Seeking Independence Using Biologically-Inspired ANN’s