book

Digital Signal Processing with Kernel Methods

by José Luis Rojo-Álvarez, Manel Martínez-Ramón, Jordi Muñoz-Marí, Gustau Camps-Valls

February 2018

Intermediate to advanced

672 pages

22h 55m

English

Wiley-IEEE Press

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Why Did We Write This Book?Structure and Contents
1.1 A New Science is Born: Signal Processing1.2 From Analog to Digital Signal Processing1.3 Digital Signal Processing Meets Machine Learning1.4 Recent Machine Learning in Digital Signal Processing
2.1 Outline of the Signal Processing Field2.2 From Time–Frequency to Compressed Sensing2.3 Multidimensional Signals and Systems2.4 Spectral Analysis on Manifolds2.5 Tutorials and Application Examples2.6 Questions and Problems
3.1 Introduction3.2 Vector Spaces, Basis, and Signal Models3.3 Digital Signal Processing Models3.4 Tutorials and Application Examples3.5 Questions and Problems3.A MATLAB simpleInterp Toolbox Structure

4.1 Introduction4.2 Kernel Functions and Mappings4.3 Kernel Properties4.4 Constructing Kernel Functions4.5 Complex Reproducing Kernel in Hilbert Spaces4.6 Support Vector Machine Elements for Regression and Estimation4.7 Tutorials and Application Examples4.8 Concluding Remarks4.9 Questions and Problems
5.1 Introduction5.2 A Framework for Support Vector Machine Signal Estimation5.3 Primal Signal Models for Support Vector Machine Signal Processing5.4 Tutorials and Application Examples5.5 Questions and Problems
6.1 Introduction6.2 Reproducing Kernel Hilbert Space Signal Models6.3 Tutorials and Application Examples6.4 Questions and Problems
7.1 Introduction7.2 Dual Signal Model Elements7.3 Dual Signal Model Instantiations7.4 Tutorials and Application Examples7.5 Questions and Problems
8.1 Introduction8.2 Kernel‐Based Regression Methods8.3 Bayesian Nonparametric Kernel Regression Models8.4 Tutorials and Application Examples8.5 Concluding Remarks8.6 Questions and Problems
9.1 Introduction9.2 Linear Adaptive Filtering9.3 Kernel Adaptive Filtering9.4 Kernel Least Mean Squares9.5 Kernel Recursive Least Squares9.6 Explicit Recursivity for Adaptive Kernel Models9.7 Online Sparsification with Kernels9.8 Probabilistic Approaches to Kernel Adaptive Filtering9.9 Further Reading9.10 Tutorials and Application Examples9.11 Questions and Problems
10.1 Introduction10.2 Support Vector Machine and Kernel Classifiers10.3 Advances in Kernel‐Based Classification10.4 Large‐Scale Support Vector Machines10.5 Tutorials and Application Examples10.6 Concluding Remarks10.7 Questions and Problems
11.1 Introduction11.2 Kernel Clustering11.3 Domain Description Via Support Vectors11.4 Kernel Matched Subspace Detectors11.5 Kernel Anomaly Change Detection11.6 Hypothesis Testing with Kernels11.7 Tutorials and Application Examples11.8 Concluding Remarks11.9 Questions and Problems
12.1 Introduction12.2 Multivariate Analysis in Reproducing Kernel Hilbert Spaces12.3 Feature Extraction with Kernel Dependence Estimates12.4 Extensions for Large‐Scale and Semi‐supervised Problems12.5 Domain Adaptation with Kernels12.6 Concluding Remarks12.7 Questions and Problems

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References

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