I have a matrix of 35 columns, and I'm trying to reduce the dimension using PCA.
I run PCA on my data:
[coeff,score,latent,tsquared,explained,mu] = pca(data);
Then, by looking at explained vector I notice the value of the first is 99, so based on this I decided to take only the first compoenet. So I did the follwoing:
X = bsxfun(@minus, data, mean(data)) * coeff(:, 1:k);
and Now, I used X for SVM training,
svmStruct = fitcsvm(X,Y,'Standardize',true, 'Prior','uniform','KernelFunction','linear','KernelScale','auto','Verbose',0,'IterationLimit', 1000000);
However, when I tried to do predict and calculate the miss-classification rate:
[label,score,cost]= predict(svmStruct, X) ;
The result was disappointing. I notice, when I select only one component (k=1), I got all classification wrong,however, as I increase number of included component (k), result is improving, as you can see from below diagram, but this doesn't make since according to explained, I should be fine with the first eginvector only.
Did I do any mistake?
Below diagram shows classification error for each number of included eginvector.