Large Scale Max-Margin Multi-Label Classification with Priors

We propose a max-margin formulation for the multi-label classification problem where the goal is to tag a data point with a set of pre-specified labels. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Existing solutions take either of two approaches. The first assumes, a priori, that there are no label correlations and independently trains a classifier for each label (as is done in the 1-vs-All heuristic). This reduces the problem complexity from exponential to linear and such methods can scale to large problems. The second approach explicitly models correlations by pairwise label interactions. However, the complexity remains exponential unless one assumes that label correlations are sparse. Furthermore, the learnt correlations reflect the training set biases. We take a middle approach that assumes labels are correlated but does not incorporate pairwise label terms in the prediction function. We show that the complexity can still be reduced from exponential to linear while modelling dense pairwise label correlations. By incorporating correlation priors we can overcome training set biases and improve prediction accuracy. We provide a principled interpretation of the 1-vs-All method and show