SparseGaussian Class

Note: This API is now obsolete.

Sparse Gaussian Kernel.

Inheritance Hierarchy

SystemObject
  Accord.Statistics.KernelsKernelBaseDouble
    Accord.Statistics.KernelsKernelBase
      Accord.Statistics.Kernels.SparseSparseGaussian

Namespace: Accord.Statistics.Kernels.Sparse
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0

Syntax

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[SerializableAttribute]
[ObsoleteAttribute("Please use the Gaussian kernel with Sparse<double> instead.")]
public sealed class SparseGaussian : KernelBase, 
	IKernel, IKernel<double[]>, IDistance, IDistance<double[]>, 
	IDistance<double[], double[]>, IReverseDistance

<SerializableAttribute>
<ObsoleteAttribute("Please use the Gaussian kernel with Sparse<double> instead.")>
Public NotInheritable Class SparseGaussian
	Inherits KernelBase
	Implements IKernel, IKernel(Of Double()), 
	IDistance, IDistance(Of Double()), IDistance(Of Double(), Double()), 
	IReverseDistance

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The SparseGaussian type exposes the following members.

Constructors

	Name	Description
	SparseGaussian	Constructs a new Sparse Gaussian Kernel
	SparseGaussian(Double)	Constructs a new Sparse Gaussian Kernel

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Properties

	Name	Description
	Gamma	Gets or sets the gamma value for the kernel. When setting gamma, sigma gets updated accordingly (gamma = 0.5*/sigma^2).
	Sigma	Gets or sets the sigma value for the kernel. When setting sigma, gamma gets updated accordingly (gamma = 0.5*/sigma^2).

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Methods

	Name	Description
	Distance	Computes the distance in input space between two points given in feature space. (Overrides KernelBaseTInputDistance(TInput, TInput).)
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Estimate	Estimate appropriate values for sigma given a data set.
	Function	Gaussian Kernel function. (Overrides KernelBaseTInputFunction(TInput, TInput).)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	ReverseDistance	Computes the squared distance in input space between two points given in feature space.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Extension Methods

	Name	Description
	Distance	Computes the kernel distance for a kernel function even if it doesn't implement the IDistance interface. Can be used to check the proper implementation of the distance function. (Defined by Tools.)
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Remarks

The Gaussian kernel requires tuning for the proper value of σ. Different approaches to this problem includes the use of brute force (i.e. using a grid-search algorithm) or a gradient ascent optimization.

For an example on how to create a sparse kernel, please see the SparseLinear page.

References:

P. F. Evangelista, M. J. Embrechts, and B. K. Szymanski. Some Properties of the Gaussian Kernel for One Class Learning. Available on: http://www.cs.rpi.edu/~szymansk/papers/icann07.pdf

Reference

Accord.Statistics.Kernels.Sparse Namespace