You could load the data matrix in MATLAB as any regular MAT-file:
load data.mat
then use the MEX function libsvmwrite
which comes with the libsvm MATLAB interface, to write it to the so called "sparse" format:
libsvmwrite('data.txt', label_vector, instance_matrix)
If you are talking about trained models not data, a quick search revealed this page (I haven't personally tested it).
EDIT:
Ok, it appears that the code I mentioned needs some tweaking. Below is my modified version. I tested it using the latest libSVM-3.12, with VS2010 as compiler:
svm_savemodel.c
#include "../svm.h"
#include "mex.h"
#include "svm_model_matlab.h"
static void fake_answer(mxArray *plhs[])
{
plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL);
}
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
struct svm_model *model;
char *filename;
const char *error_msg;
int status;
// check input
if(nrhs != 2) {
mexPrintf("Usage: svm_savemodel(model, 'filename');
");
fake_answer(plhs);
return;
}
if(!mxIsStruct(prhs[0])) {
mexPrintf("model file should be a struct array
");
fake_answer(plhs);
return;
}
if(!mxIsChar(prhs[1]) || mxGetM(prhs[1])!=1) {
mexPrintf("filename should be given as char(s)
");
fake_answer(plhs);
return;
}
// convert MATLAB struct to C struct
model = matlab_matrix_to_model(prhs[0], &error_msg);
if(model == NULL) {
mexPrintf("Error: can't read model: %s
", error_msg);
fake_answer(plhs);
return;
}
// get filename
filename = mxArrayToString(prhs[1]);
// save model to file
status = svm_save_model(filename,model);
if (status != 0) {
mexWarnMsgTxt("Error occured while writing to file.");
}
// destroy model
svm_free_and_destroy_model(&model);
mxFree(filename);
// return status value (0: success, -1: failure)
plhs[0] = mxCreateDoubleScalar(status);
return;
}
Assuming you compiled the above MEX file, here is an example usage:
[labels, data] = libsvmread('./heart_scale');
model = svmtrain(labels, data, '-c 1 -g 0.07');
svm_savemodel(model, 'mymodel.model');
The text file created looks like:
mymodel.model
svm_type c_svc
kernel_type rbf
gamma 0.07
nr_class 2
total_sv 130
rho 0.426412
label 1 -1
nr_sv 63 67
SV
1 1:0.166667 2:1 3:-0.333333 4:-0.433962 5:-0.383562 6:-1 7:-1 8:0.0687023 9:-1 10:-0.903226 11:-1 12:-1 13:1
0.6646947579781318 1:0.125 2:1 3:0.333333 4:-0.320755 5:-0.406393 6:1 7:1 8:0.0839695 9:1 10:-0.806452 12:-0.333333 13:0.5
.
.
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