Abstract

Label propagation is a popular graph-based semisupervised learning framework. So as to obtain the optimal labeling scores, the label propagation algorithm requires an inverse matrix which incurs the high computational cost of where n and c are the numbers of data points and labels, respectively. This paper proposes an efficient label propagation algorithm that guarantees exactly the same labeling results as those yielded by optimal labeling scores. The key to our approach is to iteratively compute lower and upper bounds of labeling scores to prune unnecessary score computations. This idea significantly reduces the computational cost to O(cnt) where t is the average number of iterations for each label and t n in practice. Experiments demonstrate the significant superiority of our algorithm over existing label propagation methods.