Graph-based semi-supervised learning with manifold preprocessing for image classification

In real worlds applications, some former research papers have shown that manifold learning tries to discover the non-linear low-dimensional data manifold from a high-dimensional space. Many natural images and the face images are believed to be sampled from a manifold. In this paper, we try to investigate whether discovering such manifold can aid the semi-supervised learning algorithms. We propose a novel graph-based learning algorithm locality preserving graph-based semi-supervised method (LLGSM), which firstly use both labeled and unlabeled examples as unlabeled to discover the manifolds of the data samples and then use the projected labeled examples together with projected unlabeled ones to do classification. Experiments performed on some public image data sets have demonstrated the effectiveness of our algorithm.