A one-versus-all linear classifier learns a linear function f c ∈ R n → R for each class c ∈ C. The classifier assigns to a vector X the best matching class based on these functions: Due to the linearity of the functions f c , the vector space is partitioned into convex polytopes, which each represent exactly one class (see Appendix A).
the classification accuracies can also be interpreted as supervised clustering accuracies.