Optimal Graph Learning-Based Label Propagation for Cross-Domain Image Classification

Abstract

Label propagation (LP) is a popular semi-supervised learning technique that propagates labels from a training dataset to a test one using a similarity graph, assuming that nearby samples should have similar labels. However, the recent cross-domain problem assumes that training (source domain) and test data sets (target domain) follow different distributions, which may unexpectedly degrade the performance of LP due to small similarity weights connecting the two domains. To address this problem, we propose optimal graph learning-based label propagation (OGL2P), which optimizes one cross-domain graph and two intra-domain graphs to connect the two domains and preserve domain-specific structures, respectively. During label propagation, the cross-domain graph draws two labels close if they are nearby in feature space and from different domains, while the intra-domain graph pulls two labels close if they are nearby in feature space and from the same domain. This makes label propagation more insensitive to cross-domain problems. During graph embedding, we optimize the three graphs using features and labels in the embedded subspace to extract locally discriminative and domain-invariant features and make the graph construction process robust to noise in the original feature space. Notably, as a more relaxed constraint, locally discriminative and domain-invariant can somewhat alleviate the contradiction between discriminability and domain-invariance. Finally, we conduct extensive experiments on five cross-domain image classification datasets to verify that OGL2P outperforms some state-of-the-art cross-domain approaches.