Abstract Obtaining data with meaningful labels is often costly and error-prone. In this situation, semi-supervised learning (SSL) approaches are interesting, as they leverage assumptions about the unlabeled data to make up for the limited amount of labels. However, in real-world situations, we cannot assume that the labeling process is infallible, and the accuracy of many SSL classifiers decreases significantly in the presence of label noise. In this work, we introduce the LGC _ LVOf , a leave-one-out filtering approach based on the Local and Global Consistency (LGC) algorithm. Our method aims to detect and remove wrong labels, and thus can be used as a preprocessing step to any SSL classifier. Given the propagation matrix, detecting noisy labels takes O ( c l ) per step, with c the number of classes and l the number of labels. Moreover, one does not need to compute the whole matrix, but only a l × l submatrix corresponding to interactions between labeled instances. As a result, our approach is best suited to datasets with a large amount of unlabeled data but not many labels. Results are provided for a number of datasets, including MNIST and ISOLET . LGC _ LVOf appears to be equally or more precise than the adapted gradient-based filter, and thus can be used in practice for active learning, where it may iteratively send labels for re-evaluation. We show that the best-case accuracy of the embedding of LGC _ LVOf into LGC yields performance comparable to the best-case of l1-based classifiers designed to be robust to label noise.