# using precomputed kernels with libsvm

I'm currently working on classifying images with different image-descriptors. Since they have their own metrics, I am using precomputed kernels. So given these NxN kernel-matrices (for a total of N images) i want to train and test a SVM. I'm not very experienced using SVMs though.

What confuses me though is how to enter the input for training. Using a subset of the kernel MxM (M being the number of training images), trains the SVM with M features. However, if I understood it correctly this limits me to use test-data with similar amounts of features. Trying to use sub-kernel of size MxN, causes infinite loops during training, consequently, using more features when testing gives poor results.

This results in using equal sized training and test-sets giving reasonable results. But if i only would want to classify, say one image, or train with a given amount of images for each class and test with the rest, this doesn't work at all.

How can i remove the dependency between number of training images and features, so i can test with any number of images?

I'm using libsvm for MATLAB, the kernels are distance-matrices ranging between [0,1].

## Answers

You seem to already have figured out the problem... According to the README file included in the MATLAB package:

To use precomputed kernel, you must include sample serial number as the first column of the training and testing data.

Let me illustrate with an example:

%# read dataset [dataClass, data] = libsvmread('./heart_scale'); %# split into train/test datasets trainData = data(1:150,:); testData = data(151:270,:); trainClass = dataClass(1:150,:); testClass = dataClass(151:270,:); numTrain = size(trainData,1); numTest = size(testData,1); %# radial basis function: exp(-gamma*|u-v|^2) sigma = 2e-3; rbfKernel = @(X,Y) exp(-sigma .* pdist2(X,Y,'euclidean').^2); %# compute kernel matrices between every pairs of (train,train) and %# (test,train) instances and include sample serial number as first column K = [ (1:numTrain)' , rbfKernel(trainData,trainData) ]; KK = [ (1:numTest)' , rbfKernel(testData,trainData) ]; %# train and test model = svmtrain(trainClass, K, '-t 4'); [predClass, acc, decVals] = svmpredict(testClass, KK, model); %# confusion matrix C = confusionmat(testClass,predClass)

The output:

* optimization finished, #iter = 70 nu = 0.933333 obj = -117.027620, rho = 0.183062 nSV = 140, nBSV = 140 Total nSV = 140 Accuracy = 85.8333% (103/120) (classification) C = 65 5 12 38