A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning

Multi-label classification (MLC) allows complex dependencies among labels, making it more suitable to model many real-world problems. However, data annotation for training MLC models becomes much more labor-intensive due to the correlated (hence non-exclusive) labels and a potentially large and sparse label space.