什么是算法在Matlab的RBF核矩阵? [英] what is the algorithm for an RBF kernel matrix in Matlab?

问题描述

如果我定的训练数据集和未标记的数据集，什么是Matlab的RBF核矩阵算法?

If I am given training data sets and unlabeled datasets, what is the RBF Kernel matrix algorithm for Matlab?

推荐答案

这应该是你在找什么.它是由此处

This should be what you are looking for. It is taken from here

% With Fast Computation of the RBF kernel matrix
% To speed up the computation, we exploit a decomposition of the Euclidean distance (norm)
%
% Inputs:
%       ker:    'lin','poly','rbf','sam'
%       X:      data matrix with training samples in rows and features in columns
%       X2:     data matrix with test samples in rows and features in columns
%       sigma: width of the RBF kernel
%       b:     bias in the linear and polinomial kernel
%       d:     degree in the polynomial kernel
%
% Output:
%       K: kernel matrix
%
% Gustavo Camps-Valls
% 2006(c)
% Jordi ([e-mail]), 2007
% 2007-11: if/then -> switch, and fixed RBF kernel

function K = kernelmatrix(ker,X,X2,sigma)

switch ker
    case 'lin'
        if exist('X2','var')
            K = X' * X2;
        else
            K = X' * X;
        end

    case 'poly'
        if exist('X2','var')
            K = (X' * X2 + b).^d;
        else
            K = (X' * X + b).^d;
        end

    case 'rbf'

        n1sq = sum(X.^2,1);
        n1 = size(X,2);

        if isempty(X2);
            D = (ones(n1,1)*n1sq)' + ones(n1,1)*n1sq -2*X'*X;
        else
            n2sq = sum(X2.^2,1);
            n2 = size(X2,2);
            D = (ones(n2,1)*n1sq)' + ones(n1,1)*n2sq -2*X'*X2;
        end;
        K = exp(-D/(2*sigma^2));

    case 'sam'
        if exist('X2','var');
            D = X'*X2;
        else
            D = X'*X;
        end
        K = exp(-acos(D).^2/(2*sigma^2));

    otherwise
        error(['Unsupported kernel ' ker])
end

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