数据相似Data

Synopsis

This operator measures the similarity of each example of the given ExampleSet with every other example of the same ExampleSet and returns a similarity ExampleSet.

Description

The Data to Similarity Data operator calculates the similarity among all examples of an ExampleSet. Examples are even compared to themselves. Thus if there arenexamples in the ExampleSet, this operator returnsn^2similarity comparisons. This operator provides many different measures for similarity computation. The measure to use for calculating the similarity can be specified through the parameters. Four types of measures are provided:mixed measures,nominal measures,numerical measuresandBregman divergences. Please note that the data set created by this operator is merely a view, so there should be no memory problems.

The behavior of this operator can be considered close to a certain scenario of the Cross Distances operator, if the same ExampleSet is provided at both inputs of the Cross Distances operator and thecompute similaritiesparameter is also set to false. In this case the Cross Distances operator behaves similar to the Data to Similarity Data operator. Besides sorting order, there is no major difference between these two scenarios.

Differentiation

数据相似

The Data to Similarity operator calculates the similarity among examples of an ExampleSet. Same comparisons are not repeated again e.g. if examplex相比之下,例子y计算相似性然后考试pleywill not be compared again with examplexto compute similarity because the result will be the same. Thus if there arenexamples in the ExampleSet, this operator does not returnn^2similarity comparisons. Instead it returns(n)(n-1)/2similarity comparisons. Moreover, this operator returns a similarity measure object instead of an ExampleSet.

Input

example set

This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input.

Output

similarity example set

A similarity ExampleSet that contains the calculated similarities is delivered through this port.

Parameters

Measure types

This parameter is used for selecting the type of measure to be used for calculating similarity. following options are available:mixed measures,nominal measures,numerical measuresandBregman divergences.

Mixed measure

This parameter is available if themeasure typeparameter is set to 'mixed measures'. The only available option is the 'Mixed Euclidean Distance'

Nominal measure

This parameter is available if themeasure typeparameter is set to 'nominal measures'. This option cannot be applied if the input ExampleSet has numerical attributes. In this case the 'numerical measure' option should be selected.

Numerical measure

This parameter is available if themeasure typeparameter is set to 'numerical measures'. This option cannot be applied if the input ExampleSet has nominal attributes. In this case the 'nominal measure' option should be selected.

Divergence

This parameter is available if themeasure typeparameter is set to 'bregman divergences'.

Kernel type

This parameter is only available if thenumerical measureparameter is set to 'Kernel Euclidean Distance'. The type of the kernel function is selected through this parameter. Following kernel types are supported:

dot: The dot kernel is defined byk(x,y)=x*yi.e.it is the inner product ofxandy.
radial: The radial kernel is defined byexp(-g ||x-y||^2)wheregis thegammathat is specified by thekernel gammaparameter. The adjustable parametergammaplays a major role in the performance of the kernel, and should be carefully tuned to the problem at hand.
polynomial:我多项式内核s defined byk(x,y)=(x*y+1)^dwheredis the degree of the polynomial and it is specified by thekernel degreeparameter. The Polynomial kernels are well suited for problems where all the training data is normalized.
neural: The neural kernel is defined by a two layered neural nettanh(a x*y+b)whereaisalphaandbis theintercept constant. These parameters can be adjusted using thekernel aandkernel bparameters. A common value foralphais 1/N, where N is the data dimension. Note that not all choices ofaandblead to a valid kernel function.
sigmoid: This is the sigmoid kernel. Please note that thesigmoidkernel is not valid under some parameters.
anova: This is the anova kernel. It has the adjustable parametersgammaanddegree.
epachnenikov: The Epanechnikov kernel is this function(3/4)(1-u2)forubetween -1 and 1 and zero foruoutside that range. It has the two adjustable parameterskernel sigma1andkernel degree.
gaussian_combination: This is the gaussian combination kernel. It has the adjustable parameterskernel sigma1, kernel sigma2andkernel sigma3.
multiquadric: The multiquadric kernel is defined by the square root of||x-y||^2 + c^2. It has the adjustable parameterskernel sigma1andkernel sigma shift.

Kernel gamma

This is the SVM kernel parameter gamma. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set toradialoranova.

Kernel sigma1

This is the SVM kernel parameter sigma1. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set toepachnenikov,gaussian combinationormultiquadric.

Kernel sigma2

This is the SVM kernel parameter sigma2. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set togaussian combination.

Kernel sigma3

This is the SVM kernel parameter sigma3. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set togaussian combination.

Kernel shift

This is the SVM kernel parameter shift. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set tomultiquadric.

Kernel degree

This is the SVM kernel parameter degree. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set topolynomial,anovaorepachnenikov.

Kernel a

This is the SVM kernel parameter a. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set toneural.

Kernel b

This is the SVM kernel parameter b. This parameter is only available when thenumerical measureparameter is set to 'Kernel Euclidean Distance' and thekernel typeparameter is set toneural.