This is the page for the book Digital Signal Processing with Kernel Methods.

• Image denosing and coding: ViStaCoRe: https://isp.uv.es/soft_imvideo.html

• Interpolation: simpleInterp: https://www.tsc.urjc.es/jlrojo

• Kernel gamma and kernel ARMA: https://isp.uv.es/soft_regression.html
• KARMA: Kernel AutoRegressive Moving Average with SVM: https://isp.uv.es/code/regression/karma.zip
• ARX-RVM: Autorregressive eXogenous Relevance Vector Machine: https://isp.uv.es/code/regression/karma_rvm.zip

• Simple Regression Toolbox, simpleR: https://isp.uv.es/soft_regression.html

• MSVR: Multioutput Support Vector Regression: https://isp.uv.es/code/regression/msvr-2-1.zip

• Semisupervised SVR: https://isp.uv.es/soft_regression.html, https://isp.uv.es/code/regression/demoSemiSVR.zip

• e-Huber: Epsilon-Huber Support Vector Regression: https://isp.uv.es/code/regression/svr_e_huber.zip

• KSNR: Kernel Signal to Noise Ratio: https://isp.uv.es/code/regression/ksnr.zip

• WGPR: Warped GPR: https://www.tsc.uc3m.es/~miguel/downloads.php

• VHGPR: Variational Heteroscedastic GPR: https://www.tsc.uc3m.es/~miguel/downloads.php

• Relevance vector machine (RVM): Two available toolboxes are suggested:

  1. Original: https://www.miketipping.com/sparsebayes.htm
  2. Multivariate RVM (MRVM) implementation by Arasanathan Thayananthan ([email protected])(c) Copyright University of Cambridge. Please cite the original implementation when appropriate: Multivariate Relevance Vector Machines for Tracking, Arasanathan Thayananthan et al. (University of Cambridge). In Proc. 9th European Conference on Computer Vision 2006.
  • NOTE: Wrapper functions to the later MRVM are included in simpleR: https://isp.uv.es/soft_regression.html

• SVR: The original source code of libsvm can be obtained from https://www.csie.ntu.edu.tw/~cjlin/libsvm/. The same toolbox with some modifications an additions at https://www.uv.es/jordi/soft.htm

• KARMA: Kernel AutoRegressive Moving Average with SVM: https://isp.uv.es/code/regression/karma.zip

• ARX-RVM: Autorregressive eXogenous Relevance Vector Machine: https://isp.uv.es/code/regression/karma_rvm.zip

• Kernel Adaptive Filtering Toolbox (KAFBOX): https://github.com/steven2358/kafbox/

• SVM:
  • libSVM can be obtained from https://www.csie.ntu.edu.tw/~cjlin/libsvm/
  • Alternative toolbox with some additions at https://www.uv.es/jordi/soft.htm
  • General-purpose toolboxes for classification: https://isp.uv.es/soft_classification.html
  • LS-SVM: https://www.esat.kuleuven.be/sista/lssvmlab/
  • KFD: https://github.com/lawrennd/nkfd
• Laplacian SVM:
  • https://www.dii.unisi.it/~melacci/lapsvmp/
  • https://manifold.cs.uchicago.edu/manifold_regularization/software.html
• Transductive SVM: https://svmlight.joachims.org/
• Semisupervised learning: An interesting collection of semisupervised learning software is available at https://pages.cs.wisc.edu/~jerryzhu/ssl/software.html
• Structured-output SVM:
  • Original SVM$ struct toolbox: https://svmlight.joachims.org/
  • Some useful wrapper functions at https://www.robots.ox.ac.uk/~vedaldi/svmstruct.html
• Large Margin Filtering: https://remi.flamary.com/soft/soft-filtersvm.html
• Active Learning: Active Learning MATLAB(tm) Toolbox at https://github.com/IPL-UV/altoolbox/

• Density estimation: MATLAB KDE toolbox: https://www.ics.uci.edu/~ihler/code/kde.html
• One-Class SVM:
  • libSVM can be obtained from https://www.csie.ntu.edu.tw/~cjlin/libsvm/
  • Alternative toolbox with some additions at https://www.uv.es/jordi/soft.htm
• One-Class Support Measure Machine, OCSMM: https://webdav.tuebingen.mpg.de/smm/
• Lots of one-class methods, not necessarily kernels:
  • dd_tools: https://prlab.tudelft.nl/david-tax/dd_tools.html

• SIMFEAT, A simple Feature Extraction Toolbox: https://isp.uv.es/soft_feature.html
• Kernel dependence estimates:
  • A set of MATLAB toolbox for HSIC can be found at https://www.gatsby.ucl.ac.uk/~gretton/indepTestFiles/indep.htm
  • Information Theoretical Estimators (ITE) Toolbox: https://bitbucket.org/szzoli/ite/
  • For HSIC-based feature extraction methods, use SIMFEAT toolbox and gKDR software at https://www.ism.ac.jp/~fukumizu/software.html
  • FastICA: https://research.ics.aalto.fi/ica/fastica/
  • KICA: https://www.di.ens.fr/~fbach/kernel-ica/, https://people.kyb.tuebingen.mpg.de/arthur/fastkica.htm
• Domain adaptation:
  • Kernel mean matching (KMM): https://www.cs.cmu.edu/~arthurg/covariateShiftFiles/covariateShiftSoftware.html
  • Transfer component analysis, TCA: https://github.com/viggin/domain-adaptation-toolbox
  • KEMA, kernel manifold alignment: https://github.com/dtuia/KEMA/