Robust Label Propagation and Graph Embedding for Cross-Domain Image Classification

Cross-domain label propagation (LP) faces two main challenges: 1) learning domain-invariant and 2) discriminative feature representations and obtaining high-confidence predicted labels. The distribution differences between domains can make labels difficult to propagate across domains. Low-quality labels can distort the modeling process associated with label-induced loss, resulting in decreased performance. We propose a novel cross-domain image classification method, namely, robust LP and graph embedding (RLPGE). We introduce a nuclear norm maximization constraint in order to make the predicted labels more diverse in categories while preserving their discriminability. The graph embedding process brings two nearby same-class samples close in the embedding subspace, ensuring domain invariance and local discriminability of the embedded features. For optimal graph learning, we simultaneously optimize the cross-domain graph and two intradomain graphs using both features and labels, enhancing their local discriminability and robustness to feature noise. We conducted comprehensive experiments on four cross-domain image classification datasets. The results demonstrate that our proposed RLPGE method outperforming some state-of-the-art approaches