Kernel Methods in Statistic and Machine Learning

Last update: 25 Aug 2025 13:31
First version: 15 May 2019

---

Yet Another Inadequate Placeholder

I mean in the sense of kernels used to measure how similar two objects or data-points are, rather than (or as well as) things like kernel regression smoothing (Nadaraya-Watson smoothing) or density estimation, where the kernel is used to smooth out data.

See also:
• Data Mining
• Hilbert Space Methods for Statistics and Probability
• Kernelized Factor Models
• Random Feature Methods in Statistics and Machine Learning [Originally, a Cool Trick for making kernel methods faster, but of independent interest]
• Regression, especially Nonparametric Regression
• Statistics

Recommended, big picture:
• John Shawe-Taylor and Nello Cristianini, Kernel Methods for Pattern Analysis

Recommended, historical interest:
• Emanuel Parzen [Discussed elsewhere]
  • "A New Approach to the Synthesis of Optimal Smoothing and Prediction Systems", pp. 75--108 in Richard Bellman (ed.), Mathematical Optimization Techniques: Papers presented at the Symposium on Mathematical Optimization Techniques, Santa Monica, California, October 18--20, 1960 (Berkeley: University of California Press, 1963)
  • "An Approach to Time Series Analysis", The Annals of Mathematical Statistics 32 (1961): 951--989 [JSTOR]

Modesty forbids me to recommend:
• Lecture 15 for 36-462, Data Mining / Methods of Statistical Learning [I've written other accounts of kernel methods for students over the years, but this is one of the more fleshed out, and the last section explains the different meanings of "kernel" in statistics, and how they all come out of functional analysis]

To read:
• Arvind Agarwal, Hal Daume III, "Generative Kernels for Exponential Families", AISTATS 2011
• Arash A. Amini and Zahra S. Razaee, "Concentration of kernel matrices with application to kernel spectral clustering", Annals of Statistics 49 (2021): 531--556
• Marco Cuturi and Kenji Fukumizu, "Multiresolution Kernels", cs.LG/0507033
• Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens and Bart De Moor, "Kernel Regression in the Presence of Correlated Errors", Journal of Machine Learning Research 12 (2011): 1955--1976
• Michiel Debruyne, Mia Hubert, Johan A.K. Suykens, "Model Selection in Kernel Based Regression using the Influence Function", Journal of Machine Learning Research 9 (2008): 2377--2400
• Robert Hable, "Asymptotic Confidence Sets for General Nonparametric Regression and Classification by Regularized Kernel Methods", arxiv:1203.4354
• Nancy Heckman, "The theory and application of penalized methods or Reproducing Kernel Hilbert Spaces made easy", arxiv:1111.1915
• Thomas Jaki and and R. Webster West, "Maximum Kernel Likelihood Estimation", Journal of Computational and Graphical Statistics 17 (2008): 976--993
• Johan A. K. Suykens, Carlos Alzate, and Kristiaan Pelckmans, "Primal and dual model representations in kernel-based learning", Statistics Surveys 4 (2010): 148--183

Notebooks : Powered by Blosxom