libsdca is a library for multiclass classification based on stochastic dual coordinate ascent (SDCA).

Features:

• Matlab interface
• Top-k Multiclass SVM
• Multiclass SVM of Crammer and Singer
• Proximal operators including efficient projections onto the unit simplex and the top-k simplex

The library is currently in active development and more features are planned.

libsdca can be installed on Linux and OS X in two ways.

• Using a make script in Matlab:

run matlab/make.m

• Using CMake (requires CMake version >= 2.8.9):

mkdir build && cd build && cmake .. && make install -j2

Once libsdca is compiled, add the corresponding directory to the Matlab search path:

addpath matlab

The Matlab interface is installed to the directory matlab, which should contain two mex files:

• libsdca_prox provides proximal operators;
• libsdca_solve provides solvers for multiclass classification.

To train the Multiclass SVM of Crammer and Singer on some random data, run

model = libsdca_solve(randn(2,15),randi(3,15,1))

To check the top-k training accuracies, see

model.evals(end).accuracy

To train the Top-k Multiclass SVM, specify the corresponding objective and the k

model = libsdca_solve(randn(2,15),randi(3,15,1),struct('objective','topk_svm','k',2))

Type libsdca_prox('help') and libsdca_solve('help') for further information.

Please cite libsdca in your publications if it helps your research:

@inproceedings{lapin2015topk,
  title = {Top-k Multiclass {SVM}},
  author = {Lapin, Maksim and Hein, Matthias and Schiele, Bernt},
  booktitle = {NIPS},
  year = {2015}
}