Preface |
Introduction / 1: |
Mathematical Preliminaries / Part I: |
Random Vectors and Independence / 2: |
Gradients and Optimization Methods / 3: |
Estimation Theory / 4: |
Information Theory / 5: |
Principal Component Analysis and Whitening / 6: |
Basic Independent Component Analysis / Part II: |
What is Independent Component Analysis? / 7: |
ICA by Maximization of Nongaussianity / 8: |
ICA by Maximum Likelihood Estimation / 9: |
ICA by Minimization of Mutual Information / 10: |
ICA by Tensorial Methods / 11: |
ICA by Nonlinear Decorrelation and Nonlinear PCA / 12: |
Practical Considerations / 13: |
Overview and Comparison of Basic ICA Methods / 14: |
Extensions and Related Methods / Part III: |
Noisy ICA / 15: |
ICA with Overcomplete Bases / 16: |
Nonlinear ICA / 17: |
Methods using Time Structure / 18: |
Convolutive Mixtures and Blind Deconvolution / 19: |
Other Extensions / 20: |
Applications of ICA / Part IV: |
Feature Extraction by ICA / 21: |
Brain Imaging Applications / 22: |
Telecommunications / 23: |
Other Applications / 24: |
References |
Index |
Preface |
Introduction / 1: |
Mathematical Preliminaries / Part I: |
Random Vectors and Independence / 2: |
Gradients and Optimization Methods / 3: |
Estimation Theory / 4: |