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...
| Main Author: | |
|---|---|
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
London :
Springer London : Imprint : Springer,
2000.
|
| Series: | Perspectives in neural computing.
|
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Part I
- Incorporating Temporal Effects into ICA Models: 1. Hidden Markov Independent Component Analysis 2. Particle Filters for Non-Stationary ICA
- Part II
- Considering the Validity of the Independence Assumption: 3. The Independence Assumption: Analysing the Independence of the Components by Topography 4. The Independence Assumption: Dependent Component Analysis
- Part III
- Ensemble Learning and Applications to Nonlinear ICA and Image Processing: 5. Ensemble Learning 6. Bayesian Nonlinear Independent Component Analysis by Multi-Layer Perceptrons 7. Ensemble Learning for Blind Image Separation and Deconvolution
- Part 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 Artificial Neural Networks.