Composite kernel for hyperspectral classification tools

Composite kernel 用于高光谱影像分类，其可以很好的用于空间特征与光谱特征相结合，提高高光谱影像分类精度。

function [test_bc nb_kernel_selected nb_group_selected] =                 ...
    call_gkl（ x_train y_train x_test y_test                             ...
    GKLoptions SVMoptions KERoptions all_C trial）

%%%
% _________________________________________________________________________
%
%   call_gkl.m
%   ----------
%
%   applies gkl for differents hyperparameters
% _________________________________________________________________________


disp（[‘trial ‘ num2str（trial）]）;
tolerance = 1e-5;

% create the kernel according to the option defined and x_train
% -------------------------------------------------------------
mean_x_train = mean（x_train）;
std_x_train = std（x_train 1）;
ind_zeros = find（abs（std_x_train） < tolerance）;

if ~isempty（ind_zeros）
    std_x_train（ind_zeros） = 1;
end;

n_train = length（y_train）;

x_train = x_train - repmat（mean_x_train n_train 1）;
x_train = x_train ./ repmat（std_x_train n_train 1）;
   
n_test = length（y_test）;

x_test = x_test - repmat（mean_x_train n_test 1）;
x_test = x_test ./ repmat（std_x_train n_test 1）;

% build the kernel
disp（‘create the kernel （might be long）‘）
[K] = mklbuildkernel（x_train KERoptions.type_kernel                           ...
    KERoptions.param_kernel [] [] KERoptions）;
[weightinfo_kernel] = UnitTraceNormalization（x_train                          ...
    KERoptions.type_kernel KERoptions.param_kernel                            ...
    KERoptions.variablecell）;
disp（‘kernel created‘）;

nb_kernel = size（K 3）;

for k=1:nb_kernel
    K（::k） =  K（::k） * weight（k）;
end;

nb_param = length（all_C）;
test_bc = zeros（1 nb_param）;
nb_kernel_selected = zeros（1 nb_param）;
nb_group_selected =  zeros（1 nb_param）;

% compute the solution for different hyperparameters
% --------------------------------------------------
for i=1:nb_param
    disp（[‘trial ‘ num2str（trial） ‘ param ‘ num2str（i）]）;
    [learning_sigma w b posw] = gkl_svm（K y_train all_C（i） GKLoptions SVMoptions）;

    % should not be used
    if GKLoptions.numerical_precision
        learning_sigma.weight（learning_sigma.weight < GKLoptions.numerical_precision） = 0;
    end

    ind = find（learning_sigma.weight > 0）;
    Kt = zeros（n_test length（posw））;

    for k=1:length（ind）
        var = info_kernel（ind（k））.variable;
        Kaux = svmkernel（x_test（: var） info_kernel（ind（k））.kernel                ...
            info_kernel（ind（k））.kerneloption x_train（posw var））;
        Kt = Kt + Kaux * weight（ind（k）） * learning_sigma.weight（ind（k））;
    end;

    ypred = Kt*w+b;
    test_bc（i） = mean（sign（ypred） == y_test）    
    nb_kernel_selected（i） = length（ind）
    
    nb_group = max（GKLoptions.sigma_init.group）;                    
        
    for k=1:nb_group
        ind_group = find（GKLoptions.sigma_init.group == k）;
        
        if ~isempty（intersect（ind_group ind））
            nb_group_selected（i） = nb_group_selected（i） + 1;
        end
    end
    
    nb_group_selected
end


clear K;

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----
     目录           0  2009-01-30 13:58  ckl-toolbox\
     文件         925  2009-01-16 06:00  ckl-toolbox\compute_cost_svm.m
     文件        1636  2009-01-16 05:57  ckl-toolbox\compute_golden_search.m
     文件        1289  2009-01-16 05:48  ckl-toolbox\compute_grad_svm_class.m
     文件        1956  2009-01-16 05:58  ckl-toolbox\compute_normalized_sigma.m
     文件        3213  2009-01-16 05:58  ckl-toolbox\compute_normal_vector.m
     文件         765  2009-01-16 05:46  ckl-toolbox\compute_sum_K_sigma.m
     文件         483  2009-01-16 05:42  ckl-toolbox\compute_x_to_K_sigma.m
     文件       16996  2009-01-16 06:14  ckl-toolbox\gkl_svm.m
     文件        7360  2009-01-16 05:45  ckl-toolbox\gkl_svm_update.m
     文件        2996  2009-01-16 06:10  ckl-toolbox\gkl_call.m
     文件       35147  2009-01-16 05:17  ckl-toolbox\licence.txt
     文件        6044  2009-01-30 13:58  ckl-toolbox\test_gkl_ionosphere.m
     文件        2996  2009-01-16 06:04  ckl-toolbox\call_gkl.m
     文件         416  2009-01-16 06:02  ckl-toolbox\howto.txt
     目录           0  2009-01-15 11:33  ckl-toolbox\kernel\
     目录           0  2009-01-16 06:01  ckl-toolbox\data\
     文件         877  2008-05-14 19:10  ckl-toolbox\kernel\CreateKernelListWithVariable.m
     文件        1142  2008-05-14 19:10  ckl-toolbox\kernel\mklbuildkernel.m
     文件         948  2008-05-14 19:10  ckl-toolbox\kernel\UnitTraceNormalization.m
     文件        1182  2008-05-14 19:10  ckl-toolbox\kernel\WeightK.m
     文件         747  2008-05-14 19:10  ckl-toolbox\kernel\build_efficientK.m
     目录           0  2009-01-15 11:33  ckl-toolbox\kernel\.svn\
     文件         690  2008-09-21 22:25  ckl-toolbox\kernel\.svn\all-wcprops
     文件           2  2008-09-21 22:24  ckl-toolbox\kernel\.svn\format
     文件         903  2008-12-17 08:16  ckl-toolbox\kernel\.svn\entries
     目录           0  2009-01-15 11:33  ckl-toolbox\kernel\.svn\tmp\
     目录           0  2009-01-15 11:33  ckl-toolbox\kernel\.svn\props\
     目录           0  2009-01-15 11:33  ckl-toolbox\kernel\.svn\prop-base\
     目录           0  2009-01-15 11:33  ckl-toolbox\kernel\.svn\text-base\
     目录           0  2009-01-15 11:33  ckl-toolbox\kernel\.svn\tmp\props\
............此处省略14个文件信息