Label propagation and soft-similarity measure for graph based Constrained Semi-Supervised Learning

This paper discusses a new setting of graph based semi-supervised learning (SSL) guided using pairwise constraints (PCs). Technically, we propose a novel Graph based Constrained Semi-Supervised Learning (G-CSSL) framework. In this setting, PCs are used to specify the types (intra- or inter-class) of points with labels. Because the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PCs sets using the propagated soft labels from both labeled and unlabeled data via special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced learning performance. To obtain the predicted labels of unlabeled data, we calculate the sparse codes of all data vectors jointly to assign weights for SLP. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained sample pairs by using the sparse codes and outputted probabilistic values by SLP. Extensive simulations demonstrated the effectiveness of our G-CSSL for image representation and recognition, compared with other related SSL techniques.