Advances in Independent Component Analysis
By: edited by Mark Girolami
Series: Perspectives in Neural Computing, 1431-6854; Perspectives in neural computing
Publisher: Springer London; Imprint; Springer
Published: 2000
Place of publication: London
Language: eng
Format: [electronic resource] /
Number of pages: 284
ISBN-13: 9781447104438
Table of contents: title: Part I; title: Incorporating Temporal Effects into ICA Models: 1. Hidden Markov Independent Component Analysis 2. Particle Filters for Non-Stationary ICA; title: Part II; title: 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; title: Part III; title: 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; title: Part IV; title: 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.