In recent years, Kernel methods have received major attention, particularly due to the increased popularity of the Support Vector Machines. Kernel functions can be used in many applications as they provide a simple bridge from linearity to non-linearity for algorithms which can be expressed in terms of dot products. In this article, we will list a few kernel functions and some of their properties.
- Check the source code for all kernel functions here.
Many of these functions have been incorporated in Accord.NET, a framework for creating machine learning, statistics, and computer vision applications.
- Kernel Methods
- Kernel Functions
- Linear Kernel
- Polynomial Kernel
- Gaussian Kernel
- Exponential Kernel
- Laplacian Kernel
- ANOVA Kernel
- Hyperbolic Tangent (Sigmoid) Kernel
- Rational Quadratic Kernel
- Multiquadric Kernel
- Inverse Multiquadric Kernel
- Circular Kernel
- Spherical Kernel
- Wave Kernel
- Power Kernel
- Log Kernel
- Spline Kernel
- B-Spline Kernel
- Bessel Kernel
- Cauchy Kernel
- Chi-Square Kernel
- Histogram Intersection Kernel
- Generalized Histogram Intersection Kernel
- Generalized T-Student Kernel
- Bayesian Kernel
- Wavelet Kernel
- Source code
- See also
Kernel methods are a class of algorithms for pattern analysis or recognition, whose best known element is the support vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (such as clusters, rankings, principal components, correlations, classifications) in general types of data (such as sequences, text documents, sets of points, vectors, images, graphs, etc) (Wikipedia, 2010a).
The main characteristic of Kernel Methods, however, is their distinct approach to this problem. Kernel methods map the data into higher dimensional spaces in the hope that in this higher-dimensional space the data could become more easily separated or better structured. There are also no constraints on the form of this mapping, which could even lead to infinite-dimensional spaces. This mapping function, however, hardly needs to be computed because of a tool called the kernel trick.
The Kernel trick is a very interesting and powerful tool. It is powerful because it provides a bridge from linearity to non-linearity to any algorithm that can expressed solely on terms of dot products between two vectors. It comes from the fact that, if we first map our input data into a higher-dimensional space, a linear algorithm operating in this space will behave non-linearly in the original input space.
Now, the Kernel trick is really interesting because that mapping does not need to be ever computed. If our algorithm can be expressed only in terms of a inner product between two vectors, all we need is replace this inner product with the inner product from some other suitable space. That is where resides the “trick”: wherever a dot product is used, it is replaced with a Kernel function. The kernel function denotes an inner product in feature space and is usually denoted as:
K(x,y) = <φ(x),φ(y)>
Using the Kernel function, the algorithm can then be carried into a higher-dimension space without explicitly mapping the input points into this space. This is highly desirable, as sometimes our higher-dimensional feature space could even be infinite-dimensional and thus unfeasible to compute.
Kernel functions must be continuous, symmetric, and most preferably should have a positive (semi-) definite Gram matrix. Kernels which are said to satisfy the Mercer’s theorem are positive semi-definite, meaning their kernel matrices have only non-negative Eigen values. The use of a positive definite kernel insures that the optimization problem will be convex and solution will be unique.
However, many kernel functions which aren’t strictly positive definite also have been shown to perform very well in practice. An example is the Sigmoid kernel, which, despite its wide use, it is not positive semi-definite for certain values of its parameters. Boughorbel (2005) also experimentally demonstrated that Kernels which are only conditionally positive definite can possibly outperform most classical kernels in some applications.
Kernels also can be classified as anisotropic stationary, isotropic stationary, compactly supported, locally stationary, nonstationary or separable nonstationary. Moreover, kernels can also be labeled scale-invariant or scale-dependant, which is an interesting property as scale-invariant kernels drive the training process invariant to a scaling of the data.
Choosing the most appropriate kernel highly depends on the problem at hand – and fine tuning its parameters can easily become a tedious and cumbersome task. Automatic kernel selection is possible and is discussed in the works by Tom Howley and Michael Madden.
The choice of a Kernel depends on the problem at hand because it depends on what we are trying to model. A polynomial kernel, for example, allows us to model feature conjunctions up to the order of the polynomial. Radial basis functions allows to pick out circles (or hyperspheres) – in constrast with the Linear kernel, which allows only to pick out lines (or hyperplanes).
The motivation behind the choice of a particular kernel can be very intuitive and straightforward depending on what kind of information we are expecting to extract about the data. Please see the final notes on this topic from Introduction to Information Retrieval, by Manning, Raghavan and Schütze for a better explanation on the subject.
Below is a list of some kernel functions available from the existing literature. As was the case with previous articles, every LaTeX notation for the formulas below are readily available from their alternate text html tag. I can not guarantee all of them are perfectly correct, thus use them at your own risk. Most of them have links to articles where they have been originally used or proposed.
- Linear kernel documentation – linear kernel source code – how to create SVMs in .NET with Accord.NET
The Linear kernel is the simplest kernel function. It is given by the inner product <x,y> plus an optional constant c. Kernel algorithms using a linear kernel are often equivalent to their non-kernel counterparts, i.e. KPCA with linear kernel is the same as standard PCA.
The Polynomial kernel is a non-stationary kernel. Polynomial kernels are well suited for problems where all the training data is normalized.
Adjustable parameters are the slope alpha, the constant term c and the polynomial degree d.
The Gaussian kernel is an example of radial basis function kernel.
Alternatively, it could also be implemented using
The adjustable parameter sigma plays a major role in the performance of the kernel, and should be carefully tuned to the problem at hand. If overestimated, the exponential will behave almost linearly and the higher-dimensional projection will start to lose its non-linear power. In the other hand, if underestimated, the function will lack regularization and the decision boundary will be highly sensitive to noise in training data.
The exponential kernel is closely related to the Gaussian kernel, with only the square of the norm left out. It is also a radial basis function kernel.
The Laplace Kernel is completely equivalent to the exponential kernel, except for being less sensitive for changes in the sigma parameter. Being equivalent, it is also a radial basis function kernel.
It is important to note that the observations made about the sigma parameter for the Gaussian kernel also apply to the Exponential and Laplacian kernels.
The ANOVA kernel is also a radial basis function kernel, just as the Gaussian and Laplacian kernels. It is said to perform well in multidimensional regression problems (Hofmann, 2008).
The Hyperbolic Tangent Kernel is also known as the Sigmoid Kernel and as the Multilayer Perceptron (MLP) kernel. The Sigmoid Kernel comes from the Neural Networks field, where the bipolar sigmoid function is often used as an activation function for artificial neurons.
It is interesting to note that a SVM model using a sigmoid kernel function is equivalent to a two-layer, perceptron neural network. This kernel was quite popular for support vector machines due to its origin from neural network theory. Also, despite being only conditionally positive definite, it has been found to perform well in practice.
There are two adjustable parameters in the sigmoid kernel, the slope alpha and the intercept constant c. A common value for alpha is 1/N, where N is the data dimension. A more detailed study on sigmoid kernels can be found in the works by Hsuan-Tien and Chih-Jen.
The Rational Quadratic kernel is less computationally intensive than the Gaussian kernel and can be used as an alternative when using the Gaussian becomes too expensive.
The Multiquadric kernel can be used in the same situations as the Rational Quadratic kernel. As is the case with the Sigmoid kernel, it is also an example of an non-positive definite kernel.
The Inverse Multi Quadric kernel. As with the Gaussian kernel, it results in a kernel matrix with full rank (Micchelli, 1986) and thus forms a infinite dimension feature space.
The circular kernel is used in geostatic applications. It is an example of an isotropic stationary kernel and is positive definite in R2.
The spherical kernel is similar to the circular kernel, but is positive definite in R3.
The Power kernel is also known as the (unrectified) triangular kernel. It is an example of scale-invariant kernel (Sahbi and Fleuret, 2004) and is also only conditionally positive definite.
The Log kernel seems to be particularly interesting for images, but is only conditionally positive definite.
However, what it actually mean is:
The B-Spline kernel is defined on the interval [−1, 1]. It is given by the recursive formula:
In the work by Bart Hamers it is given by:
Alternatively, Bn can be computed using the explicit expression (Fomel, 2000):
Where x+ is defined as the truncated power function:
The Bessel kernel is well known in the theory of function spaces of fractional smoothness. It is given by:
The Chi-Square kernel comes from the Chi-Square distribution:
However, as noted by commenter Alexis Mignon, this version of the kernel is only conditionally positive-definite (CPD). A positive-definite version of this kernel is given in (Vedaldi and Zisserman, 2011) as
and is suitable to be used by methods other than support vector machines.
The Histogram Intersection Kernel is also known as the Min Kernel and has been proven useful in image classification.
The Generalized Histogram Intersection kernel is built based on the Histogram Intersection Kernel for image classification but applies in a much larger variety of contexts (Boughorbel, 2005). It is given by:
The Bayesian kernel could be given as:
However, it really depends on the problem being modeled. For more information, please see the work by Alashwal, Deris and Othman, in which they used a SVM with Bayesian kernels in the prediction of protein-protein interactions.
Where a and c are the wavelet dilation and translation coefficients, respectively (the form presented above is a simplification, please see the original paper for details). A translation-invariant version of this kernel can be given as:
Where in both h(x) denotes a mother wavelet function. In the paper by Li Zhang, Weida Zhou, and Licheng Jiao, the authors suggests a possible h(x) as:
Which they also prove as an admissible kernel function.
The latest version of the source code for almost all of the kernels listed above is available in the Accord.NET Framework. Some are also available in the sequel of this article, Kernel Support Vector Machines for Classification and Regression in C#. They are provided together with a comprehensive and simple implementation of SVMs (Support Vector Machines) in C#. However, for the latest sources, which may contain bug fixes and other enhancements, please download the most recent version available of Accord.NET.
- Kernel Support Vector Machines (kSVMs)
- Principal Component Analysis (PCA)
- Kernel Principal Component Analysis (KPCA)
- Linear Discriminant Analysis (LDA)
- Non-Linear Discriminant Analysis with Kernels (KDA)
- Logistic Regression Analysis in C#
- The Accord.NET Framework: Scientific Computing in .NET
- Haar-feature object detection in C# (The Viola-Jones Classifier)
- Handwriting Recognition using Kernel Discriminant Analysis
- Handwriting Recognition Revisited: Kernel Support Vector Machines
- Logistic Regression Analysis
- On-Line Prediction Wiki Contributors. “Kernel Methods.” On-Line Prediction Wiki. http://onlineprediction.net/?n=Main.KernelMethods (accessed March 3, 2010).
- Genton, Marc G. “Classes of Kernels for Machine Learning: A Statistics Perspective.” Journal of Machine Learning Research 2 (2001) 299-312.
- Hofmann, T., B. Schölkopf, and A. J. Smola. “Kernel methods in machine learning.” Ann. Statist. Volume 36, Number 3 (2008), 1171-1220.
- Gunn, S. R. (1998, May). “Support vector machines for classification and regression.” Technical report, Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science.
- Karatzoglou, A., Smola, A., Hornik, K. and Zeileis, A. “Kernlab – an R package for kernel Learning.” (2004).
- Karatzoglou, A., Smola, A., Hornik, K. and Zeileis, A. “Kernlab – an S4 package for kernel methods in R.” J. Statistical Software, 11, 9 (2004).
- Karatzoglou, A., Smola, A., Hornik, K. and Zeileis, A. “R: Kernel Functions.” Documentation for package ‘kernlab’ version 0.9-5. http://rss.acs.unt.edu/Rdoc/library/kernlab/html/dots.html (accessed March 3, 2010).
- Howley, T. and Madden, M.G. “The genetic kernel support vector machine: Description and evaluation“. Artificial Intelligence Review. Volume 24, Number 3 (2005), 379-395.
- Shawkat Ali and Kate A. Smith. “Kernel Width Selection for SVM Classification: A Meta-Learning Approach.” International Journal of Data Warehousing & Mining, 1(4), 78-97, October-December 2005.
- Hsuan-Tien Lin and Chih-Jen Lin. “A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods.” Technical report, Department of Computer Science, National Taiwan University, 2003.
- Boughorbel, S., Jean-Philippe Tarel, and Nozha Boujemaa. “Project-Imedia: Object Recognition.” INRIA – INRIA Activity Reports – RalyX. http://ralyx.inria.fr/2004/Raweb/imedia/uid84.html (accessed March 3, 2010).
- Huang, Lingkang. “Variable Selection in Multi-class Support Vector Machine and Applications in Genomic Data Analysis.” PhD Thesis, 2008.
- Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. “Nonlinear SVMs.” The Stanford NLP (Natural Language Processing) Group. http://nlp.stanford.edu/IR-book/html/htmledition/nonlinear-svms-1.html (accessed March 3, 2010).
- Fomel, Sergey. “Inverse B-spline interpolation.” Stanford Exploration Project, 2000. http://sepwww.stanford.edu/public/docs/sep105/sergey2/paper_html/node5.html (accessed March 3, 2010).
- Basak, Jayanta. “A least square kernel machine with box constraints.” International Conference on Pattern Recognition 2008 1 (2008): 1-4.
- Alashwal, H., Safaai Deris, and Razib M. Othman. “A Bayesian Kernel for the Prediction of Protein – Protein Interactions.” International Journal of Computational Intelligence 5, no. 2 (2009): 119-124.
- Hichem Sahbi and François Fleuret. “Kernel methods and scale invariance using the triangular kernel”. INRIA Research Report, N-5143, March 2004.
- Sabri Boughorbel, Jean-Philippe Tarel, and Nozha Boujemaa. “Generalized histogram intersection kernel for image recognition”. Proceedings of the 2005 Conference on Image Processing, volume 3, pages 161-164, 2005.
- Micchelli, Charles. Interpolation of scattered data: Distance matrices and conditionally positive definite functions. Constructive Approximation 2, no. 1 (1986): 11-22.
- Wikipedia contributors, “Kernel methods,” Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title=Kernel_methods&oldid=340911970 (accessed March 3, 2010).
- Wikipedia contributors, “Kernel trick,” Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title=Kernel_trick&oldid=269422477 (accessed March 3, 2010).
- Weisstein, Eric W. “Positive Semidefinite Matrix.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/PositiveSemidefiniteMatrix.html
- Hamers B. “Kernel Models for Large Scale Applications”, Ph.D. , Katholieke Universiteit Leuven, Belgium, 2004.
- Li Zhang, Weida Zhou, Licheng Jiao. Wavelet Support Vector Machine. IEEE Transactions on System, Man, and Cybernetics, Part B, 2004, 34(1): 34-39.
- Vedaldi, A. and Zisserman, A. Efficient Additive Kernels via Explicit Feature Maps. IEEE Transactions on Pattern Recognition and Machine Intelligence, Vol. XX, No. XX, June, 2011.
Citing this work
If you would like, please cite this work as: Souza, César R. “Kernel Functions for Machine Learning Applications.” 17 Mar. 2010. Web. <http://crsouza.blogspot.com/2010/03/kernel-functions-for-machine-learning.html>.