Reviews: Large Margin Discriminant Dimensionality Reduction in Prediction Space

The authors modify the MCBoost criterion, in order to allow for multi-class boosting that is based on arbitrary number of dimensions (compared to a previous formulation that limits the number of dimensions to the number of classes). This lift of the limits in terms of dimensionality allows for a boosting-like framework that is comprised of controllable amount of boosting functions, and thus can be used as. The connection between MC-Boost and MV-SVM is interesting, and the discussion is good. Is the fact that both MC-SVM and MC-Boost try to maximise the margin well known? The authors present improved results in terms of error rate, and in terms of mAP.