This code uses dense vectors to store data instances. If most feature values are non-zeros, the training/testing time is faster than the standard libsvm, which implement sparse vectors.

Experimental Setting:

We select four concepts, animal, people, sky, and weather, from MediaMill Challenge Problem for this experiment. All instances are 120-dimension dense vectors. We compare the standard libsvm and this modification.

The experimental procedure performs parameter selection (30 parameter sets), model training (best parameters), test data predictions. There are 30,993 and 12,914 records in training (used in parameter selection and model training stages) and testing (used in prediction stage) sets, respectively. The following table shows the results.

  1. parameter-selection execution time (sec.): original dense change libsvm repr. ratio animal 2483.23 1483.02 -40.3% people 22844.26 13893.21 -39.2% sky 13765.39 8460.06 -38.5% weather 2240.01 1325.32 -40.1% AVERAGE 10083.17 6540.46 -39.1%
  2. model-training execution time (sec.): original dense change libsvm repr. ratio animal 113.238 70.085 -38.1% people 725.995 451.244 -37.8% sky 1234.881 784.071 -36.5% weather 123.179 76.532 -37.9% AVERAGE 549.323 345.483 -37.1%
  3. prediction execution time (sec.): original dense change libsvm repr.. ratio animal 12.226 6.895 -43.6% people 99.268 54.855 -44.7% sky 78.069 42.417 -45.7% weather 10.843 6.495 -44.9% AVERAGE 50.102 27.666 -44.8%

Overall, dense-representation libsvm saves on average 39.1%, 37.1%, and 44.8% of the execution time in parameter selection, model training and prediction stages, respectively. This modification is thus useful for dense data sets.

Original page: https://github.com/strands-project/strands_perception_people/blob/indigo-devel/opencv_warco/src/opencv_warco/libsvm/README.dense