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Handbook of Blind Source Separation : Independent Component Analysis and Applications.
A key task of engineers is to design and analyse systems; however, they often have to do this without knowing a system's parameters. BSS is a very important area in signal processing as it enables engineers to derive the unknown inputs of a system from its known outputs. It also enables the sep...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Burlington :
Elsevier Science,
2010.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Front cover; Half page; Title page; Copyright page; Contents; About the editors; Preface; Contributors; Chapter 1. Introduction; 1.1. Genesis of blind source separation; 1.2. Problem formalization; 1.3. Source separation methods; 1.4. Spatial whitening, noise reduction and PCA; 1.5. Applications; 1.6. Content of the handbook; References; Chapter 2. Information; 2.1. Introduction; 2.2. Methods based on mutual information; 2.3. Methods based on mutual information rate; 2.4. Conclusion and perspectives; References; Chapter 3. Contrasts; 3.1. Introduction; 3.2. Cumulants; 3.3. MISO contrasts.
- 3.4. MIMO contrasts for static mixtures3.5. MIMO contrasts for dynamic mixtures; 3.6. Constructing other contrast criteria; 3.7. Conclusion; References; Chapter 4. Likelihood; 4.1. Introduction: Models and likelihood; 4.2. Transformation model and equivariance; 4.3. Independence; 4.4. Identifiability, stability, performance; 4.5. Non-Gaussian models; 4.6. Gaussian models; 4.7. Noisy models; 4.8. Conclusion: A general view; 4.9. Appendix: Proofs; References; Chapter 5. Algebraic methods after prewhitening; 5.1. Introduction; 5.2. Independent component analysis.
- 5.3. Diagonalization in least squares sense5.4. Simultaneous diagonalization of matrix slices; 5.5. Simultaneous diagonalization of third-order tensor slices; 5.6. Maximization of the tensor trace; References; Chapter 6. Iterative algorithms; 6.1. Introduction; 6.2. Model and goal; 6.3. Contrast functions for iterative BSS/ICA; 6.4. Iterative search algorithms: Generalities; 6.5. Iterative whitening; 6.6. Classical adaptive algorithms; 6.7. Relative (natural) gradient techniques; 6.8. Adapting the nonlinearities; 6.9. Iterative algorithms based on deflation; 6.10. The FastICA algorithm.
- 6.11. Iterative algorithms with optimal step size6.12. Summary, conclusions and outlook; References; Chapter 7. Second-order methods based on color; 7.1. Introduction; 7.2. WSS processes; 7.3. Problem formulation, identifiability and bounds; 7.4. Separation based on joint diagonalization; 7.5. Separation based on maximum likelihood; 7.6. Additional issues; References; Chapter 8. Convolutive mixtures; 8.1. Introduction and mixture model; 8.2. Invertibility of convolutive MIMO mixtures; 8.3. Assumptions; 8.4. Joint separating methods; 8.5. Iterative and deflation methods.
- 8.6. Non-stationary contextReferences; Chapter 9. Algebraic identification of under-determined mixtures; 9.1. Observation model; 9.2. Intrinsic identifiability; 9.3. Problem formulation; 9.4. Higher-order tensors; 9.5. Tensor-based algorithms; 9.6. Appendix: expressions of complex cumulants; References; Chapter 10. Sparse component analysis; 10.1. Introduction; 10.2. Sparse signal representations; 10.3. Joint sparse representation of mixtures; 10.4. Estimating the mixing matrix by clustering; 10.5. Square mixing matrix: Relative Newton method; 10.6. Separation with a known mixing matrix.