A unified semi-supervised model with joint estimation of graph, soft labels and latent subspace.

Since manually labeling images is expensive and labor intensive, in practice we often do not have enough labeled images to train an effective classifier for the new image classification tasks. The graph-based SSL methods have received more attention in practice due to their convexity, scalability and efficiency. In this paper, we propose a novel graph-based semi-supervised learning method that takes full advantage of a small set of labeled graphs and a large set of unlabeled graph data. First, we explain the concept of graph-based semi-supervised learning. The core idea of these models is to jointly estimate a low-rank graph with soft labels and a latent subspace. The proposed scheme leverages the synergy between the graph structure and the data representation in terms of soft labels and latent features. This improves the monitoring information and leads to better discriminative linear transformation. Several experiments were conducted on five image datasets using state-of-the-art methods. These experiments show the effectiveness of the proposed semi-supervised method.