如何标记 PCA 获得的训练投影以用于训练 SVM 进行分类？ MATLAB

Question

我有一组图像“训练集”。我已经形成了“本征空间”。现在我需要标记预测来训练 SVM。 “面 1”到特征空间的投影必须标记为 +1，所有其他面到特征空间的投影必须标记为 -1。

我不知道该怎么做。任何建议都会很有帮助！

我使用以下方法形成了特征空间：

    function [signals,V] = pca2(data)
    [M,N] = size(data); 
    data = reshape(data, M*N,1); % subtract off the mean for each dimension 
    mn = mean(data,2); 
    data = bsxfun(@minus, data, mean(data,1)); 
    % construct the matrix Y 
    Y = data'*data / (M*N-1); 
    [V D] = eigs(Y, 10); % reduce to 10 dimension 
    % project the original data 
    signals = data * V; 

Answer 1

label = ones(N,1);% N samples in total, +1 represents face 1
for i=1:N 
    % For each face image, you run
    [signals,V] = pca2(data); % ith data
    if ....  % other faces than face 1
        label(i) = -1;
    end
    face(i,:) = reshape(signals,1,[]);
end
model = svmtrain(label,face);

Answer 2

如果您要识别多个人，则必须为每个人创建一个单独的数据文件，并为每个人创建一个单独的 SVM。这是因为 SVM 专注于二分类。

这是一个在 Matlab 中使用 libsvm 的示例（here 是完整代码），假设您在文件中有数据：

[person1_label, person1_inst] = libsvmread('../person1');
[person2_label, person2_inst] = libsvmread('../person2');
[person3_label, person3_inst] = libsvmread('../person3');

model1 = svmtrain(person1_label, person1_inst, '-c 1 -g 0.07 -b 1');
model2 = svmtrain(person2_label, person2_inst, '-c 1 -g 0.07 -b 1');
model3 = svmtrain(person3_label, person3_inst, '-c 1 -g 0.07 -b 1');

要测试一张人脸，您需要应用所有模型并获得最大输出（使用svmpredict 时，您必须使用'-b 1' 来获得概率估计。

另外，在Matlab中不用svmread或svmwrite，直接传数据即可：

training_data = [];%Your matrix that contains 4 feature vectors
person1_label =[1,1,-1,-1];
person2_label = [-1,-1,1,-1];
person3_label = [-1,-1,-1,1];

model1 = svmtrain(person1_label, person_inst, '-c 1 -g 0.07 -b 1');
model2 = svmtrain(person2_label, person_inst, '-c 1 -g 0.07 -b 1');
model3 = svmtrain(person3_label, person_inst, '-c 1 -g 0.07 -b 1');

Answer 3

您似乎无法训练 SVM...这是有关如何训练 Matlab SVM 的示例：

%Generate 100 positive points (the data is a circle)
r = sqrt(rand(100,1)); % radius
t = 2*pi*rand(100,1); % angle
dataP = [r.*cos(t), r.*sin(t)]; % points

%Generate 100 negative points (the data is a circle)
r2 = sqrt(3*rand(100,1)+1); % radius
t2 = 2*pi*rand(100,1); % angle
dataN = [r2.*cos(t2), r2.*sin(t2)]; % points

data3 = [dataN;dataP];
theclass = ones(200,1);
theclass(1:100) = -1; %First 100 points are negative

%Train the SVM
cl = svmtrain(data3,theclass,'Kernel_Function','rbf');