SYSTEMS AND METHODS FOR EXPERT GUIDED SEMI-SUPERVISION WITH LABEL PROPAGATION FOR MACHINE LEARNING MODELS

A method includes receiving a labeled dataset that includes a plurality of labeled samples and initially training the machine learning model using the labeled dataset. The method also includes receiving an unlabeled dataset that includes a plurality of unlabeled samples. The method also includes computing latent representation spaces for each respective sample of the plurality of labeled samples and each respective sample of the plurality of the unlabeled samples. The method also includes generating a k-nearest neighbor similarity graph based on the latent representation spaces, generating a combined similarity graph by augmenting the k-nearest neighbor similarity graph using an expert-derived similarity graph, and propagating, using the combined similarity graph, labels to each respective sample of the plurality of unlabeled samples. The method also includes subsequently training the machine learning model using the labeled dataset and the unlabeled dataset having samples propagated with labels using the combined similarity graph.