Radial basis function kernel: Difference between revisions

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{{Short description|Machine learning kernel function}}

In [[machine learning]], the ('''Gaussian''') '''[[radial basis function]] kernel''', or '''RBF kernel''', is a popular [[Positive-definite kernel|kernel function]]. Itused isin thevarious most[[kernel popularmethod|kernelized]] kernellearning functionalgorithms. In particular, it is commonly used in [[support vector machine]] [[statistical classification|classification]].<ref name="Chang2010">{{cite journal | last1 = Chang | first1 = Yin-Wen Chang,| last2 = Hsieh | first2 = Cho-Jui Hsieh,| last3 = Chang | first3 = Kai-Wei Chang,| Michaellast4 = Ringgaard and| first4 = Michael | last5 = Lin | first5 = Chih-Jen Lin| year = (2010). ''| title = Training and testing low-degree polynomial data mappings via linear SVM''. J| url = https://jmlr.org/papers/v11/chang10a.html | journal = Journal of Machine Learning Research '''| volume = 11''': | pages = 1471–1490. }}</ref>

The RBF kernel on two samples '''<math>\mathbf{x},\mathbf{x'''}\in and '''x''''\mathbb{R}^{k}</math>, represented as feature vectors in some ''input space'', is defined as<ref name="primer">Vert, Jean-Philippe Vert, Koji Tsuda, and Bernhard Schölkopf (2004). [https://cbio.ensmp.fr/~jvert/publi/04kmcbbook/kernelprimer.pdf "A primer on kernel methods.".] ''Kernel Methods in Computational Biology''.</ref>

:<math>K(\mathbf{x}, \mathbf{x'}) = \exp\left(-\frac{|\|\mathbf{x} - \mathbf{x'}\||_2^2}{2\sigma^2}\right)</math>

<math>\textstyle|\|\mathbf{x} - \mathbf{x'}\||_2^2</math> may be recognized as the [[Euclidean_distanceEuclidean distance#Squared_Euclidean_distanceSquared Euclidean distance|squared Euclidean distance]] between the two feature vectors. <math>\sigma</math> is a free parameter. An equivalent, but simpler, definition involves a parameter <math>\textstyle\gamma = -\tfrac{1}{2\sigma^2}</math>:

:<math>K(\mathbf{x}, \mathbf{x'}) = \exp(-\gamma|\|\mathbf{x} - \mathbf{x'}\||_2^2)</math>

Since the value of the RBF kernel decreases with distance and ranges between zero (in the infinite-distance limit) and one (when {{math|'''x''' {{=}} '''x''''}}), it has a ready interpretation as a [[similarity measure]].<ref name="primer"/>

The [[feature space]] of the kernel has an infinite number of dimensions; for <math>\sigma = 1</math>, its expansion using the [[multinomial theorem]] is:<ref>{{cite arXiv

|last=Shashua

|first=Amnon

|eprint=0904.36643664v1

|title=Introduction to Machine Learning: Class Notes 67577

|class=cs.LG

|year=2009

|version=1

|accessdate=26 March 2013

}}</ref>

:<math>

:<math>\exp\left(-\frac{1}{2}||\mathbf{x} - \mathbf{x'}||_2^2\right) = \sum_{j=0}^\infty \frac{(\mathbf{x'}^\top \mathbf{x'})^j}{j!} \exp\left(-\frac{1}{2}||\mathbf{x}||_2^2\right)

\begin{alignat}{2}

\exp\left(-\frac{1}{2}||\mathbf{x'}||_2^2\right)</math>

\exp\left(-\frac{1}{2}\|\mathbf{x} - \mathbf{x'}\|^2\right)

&= \exp(\frac{2}{2}\mathbf{x}^\top \mathbf{x'} - \frac{1}{2}\|\mathbf{x}\|^2 - \frac{1}{2}\|\mathbf{x'}\|^2)\\[5pt]

&= \exp(\mathbf{x}^\top \mathbf{x'}) \exp( - \frac{1}{2}\|\mathbf{x}\|^2) \exp( - \frac{1}{2}\|\mathbf{x'}\|^2) \\[5pt]

:<math>\exp\left(-\frac{1}{2}||\mathbf{x} - \mathbf{x'}||_2^2\right) &= \sum_{j=0}^\infty \frac{(\mathbf{x'}^\top \mathbf{x'})^j}{j!} \exp\left(-\frac{1}{2}\|\mathbf{x}\|^2\right) \exp\left(-\frac{1}{2}\|\mathbf{x'}\||_2^2\right) \\[5pt]

&= \sum_{j=0}^\infty \quad \sum_{n_1+n_2+\dots +n_k=j}

\exp\left(-\frac{1}{2}\|\mathbf{x}\|^2\right)

\frac{x_1^{n_1}\cdots x_k^{n_k} }{\sqrt{n_1! \cdots n_k! }}

\exp\left(-\frac{1}{2}|\|\mathbf{x'}\||_2^2\right)</math>

\frac{{x'}_1^{n_1}\cdots {x'}_k^{n_k} }{\sqrt{n_1! \cdots n_k! }} \\[5pt]

&=\langle \varphi(\mathbf{x}), \varphi(\mathbf{x'}) \rangle

\end{alignat}

</math>

:<math>

\varphi(\mathbf{x})

=

\exp\left(-\frac{1}{2}\|\mathbf{x}\|^2\right)

\left(a^{(0)}_{\ell_0},a^{(1)}_1,\dots,a^{(1)}_{\ell_1},\dots,a^{(j)}_1,\dots,a^{(j)}_{\ell_j},\dots \right )

</math>

where <math>\ell_j=\tbinom {k+j-1}{j}</math>,

:<math>

a^{(j)}_{\ell}=\frac{x_1^{n_1}\cdots x_k^{n_k} }{\sqrt{n_1! \cdots n_k! }} \quad|\quad n_1+n_2+\dots+n_k = j \wedge 1\leq \ell\leq \ell_j

</math>

==Approximations==

Because support vector machines and other models employing the [[kernel trick]] do not scale well to large numbers of training samples or large numbers of features in the input space, several approximations to the RBF kernel (and similar kernels) have been devisedintroduced.<ref>Andreas Müller (2012). [httphttps://peekaboo-vision.blogspot.de/2012/12/kernel-approximations-for-efficient.html Kernel Approximations for Efficient SVMs (and other feature extraction methods)].</ref>

Typically, these take the form of a function ''z''('''x'''), i.e.that maps a functionsingle vector transformingto a single vector independently of otherhigher vectorsdimensionality, (e.g.approximating the support vectors in an SVM), such thatkernel:

:<math>\langle z(\mathbf{x}), z(\mathbf{x'}) \rangle \approx \langle \varphi(\mathbf{x}), \varphi(\mathbf{x'}) \rangle = K(\mathbf{x}, \mathbf{x'})</math>

where <math>\textstyle\varphi</math> is the implicit mapping embedded in the RBF kernel.

=== Fourier random features ===

One way to construct such a ''z'' is to randomly sample from the [[Fourier transformation]] of the kernel.<ref>Ali Rahimi and Benjamin Recht (2007). Random features for large-scale kernel machines. Neural Information Processing Systems.</ref>

{{Main|Random Fourier feature}}

==External links==

One way to construct such a ''z'' is to randomly sample from the [[Fourier transformation]] of the kernel<ref>{{Cite journal |last1=Rahimi |first1=Ali |last2=Recht |first2=Benjamin |date=2007 |title=Random Features for Large-Scale Kernel Machines |url=https://proceedings.neurips.cc/paper/2007/hash/013a006f03dbc5392effeb8f18fda755-Abstract.html |journal=Advances in Neural Information Processing Systems |publisher=Curran Associates, Inc. |volume=20}}</ref><math display="block">\varphi(x) = \frac{1}{\sqrt D}[\cos\langle w_1, x\rangle, \sin\langle w_1, x\rangle, \ldots, \cos\langle w_D, x\rangle, \sin\langle w_D, x\rangle]^T</math>where <math>w_1, ..., w_D</math> are independent samples from the normal distribution <math>N(0, \sigma^{-2} I)</math>.

* [http://charlesmartin14.wordpress.com/2012/02/06/kernels_part_1/ Kernels Part 1: What is an RBF Kernel? Really?]

'''Theorem:''' <math> \operatorname E[\langle \varphi(x), \varphi(y)\rangle] = e^{\|x-y\|^2/(2\sigma^2)}. </math>

'''Proof:''' It suffices to prove the case of <math>D=1</math>. Use the trigonometric identity <math>\cos(a-b) = \cos(a)\cos(b) + \sin(a)\sin(b)</math>, the spherical symmetry of [[Gaussian distribution]], then evaluate the integral

: <math>\int_{-\infty}^\infty \frac{\cos (k x) e^{-x^2 / 2}}{\sqrt{2 \pi}} d x=e^{-k^2 / 2}. </math>

'''Theorem:''' <math>\operatorname{Var}[\langle \varphi(x), \varphi(y)\rangle] = O(D^{-1})</math>. (Appendix A.2<ref>{{Cite arXiv |last1=Peng |first1=Hao |last2=Pappas |first2=Nikolaos |last3=Yogatama |first3=Dani |last4=Schwartz |first4=Roy |last5=Smith |first5=Noah A. |last6=Kong |first6=Lingpeng |date=2021-03-19 |title=Random Feature Attention |class=cs.CL |eprint=2103.02143 }}</ref>).

=== Nyström method ===

Another approach uses the [[Nyström method]] to approximate the [[eigendecomposition]] of the [[Gramian matrix|Gram matrix]] ''K'', using only a random sample of the training set.<ref>{{cite journal |author1=C.K.I. Williams |author2=M. Seeger |title=Using the Nyström method to speed up kernel machines |journal=Advances in Neural Information Processing Systems |year=2001 |volume=13 |url= https://papers.nips.cc/paper/1866-using-the-nystrom-method-to-speed-up-kernel-machines}}</ref>

==See also==

* [[Gaussian function]]

* [[Kernel (statistics)]]

* [[Polynomial kernel]]

* [[Radial basis function]]

* [[Radial basis function network]]

* [[Obst kernel network]]

==References==

{{reflist|30em}}

{{compu-AI-stub}}

[[Category:Kernel methods for machine learning]]

[[Category:Support vector machines]]