A Probabilistic Framework for Multi-Label Learning with Unseen Labels

Abstract

We present a probabilistic framework for multi-label learning for the setting when the test data may require predicting labels that were not available at training time (i.e., the zero-shot learning setting). We develop a probabilistic model that leverages the co-occurrence statistics of the labels via a joint generative model for the label matrix (which denotes the label presence/absence for each training example) and for the label co-occurrence matrix (which denotes how many times a pair of labels co-occurs with each other). In addition to handling the unseen labels at test time, leveraging the co-occurrence information may also help in the standard multi-label learning setting, especially if the number of training examples is very small and/or the label matrix of training examples has a large fraction of missing entries. Our experimental results demonstrate the efficacy of our model in handling unseen labels.