I'm writing an application that uses an SVM to do classification on some images (specifically these). My Matlab implementation works really well. Using a SIFT bag-of-words approach, I'm able to get near 100% accuracy with a linear kernel.
I need to implement this in C++ for speed/portability reasons, and so I've tried using both libsvm and dlib. I've tried multiple SVM types (c_svm, nu_svm, one_class) and multiple kernels (linear, polynomial, rbf). The best I've been able to achieve is around 50% accuracy - even on the same samples that I've trained on. I've confirmed that my feature generators are working, because when I export my c++-generated features to Matlab and train on those, I'm able to get near-perfect results again.
Is there something magical about Matlab's SVM implementation? Are there any common pitfalls or areas that I might look into that would explain the behavior I'm seeing? I know this is a little vague, but part of the problem is that I don't know where to go. Please let me know in the comments if there is other info I can provide that would be helpful.
There is nothing magical about the Matlab version of the libraries, other that it runs in Matlab which makes it harder to shoot yourself on the foot.
A check list:
- Are you normalizing your data, making all values lie between 0 and 1
(or between -1 and 1), either linearly or using the mean and the
standard deviation?
- Are you parameter searching for a good value of C (or C and gamma in
the case of an RBF kernel)? Doing cross validation or on a hold out set?
- Are you sure that your're handling NaN, and all other floating point
nastiness? Matlab is very good at hiding this from you, C++ not so
much.
- Could it be that you're loading your data incorrectly, reading a
"%s" into a double or something that is adding noise to your input
data?
- Could it be that libsvm/dlib expects the data in row major order and
your're sending it in in column major (or the other way around)? Again Matlab makes this almost impossible, C++ not so much.
- 32-64 bit nastiness one version of the library, executable compiled
with the other?
Some other things:
- Could it be that in Matlab you're somehow leaking the class (y) into
the preprocessing? no one does this on purpose, but I've seen it happen.
If you make almost any f(y) a feature, you'll get almost 100%
everytime.
- Sometimes it helps to verify that everything is numerically
identical by printing to file before training both in C++ and
Matlab.
i'm very happy with libsvm using the rbf kernel. carlosdc pointed out the most common errors in the correct order :-). for libsvm - did you use the python tools shipped with libsvm? if not i recommend to do so. write your feature vectors to a file (from matlab and/or c++) and do a metatraining for the rbf kernel with easy.py. you get the parameters and a prediction for the generated model. if this prediction is ok continue with c++. from training you also get a scaled feature file (min/max transformed to -1.0/1.0 for every feature). compare these to your c++ implementation as well.
some libsvm issues: a nasty habit is (if i remember correctly) that values scaling to 0 (zero) are omitted in the scaled file. in grid.py is a parameter "nr_local_worker" which is defining the mumber of threads. you might wish to increase it.
