Low Rank Sample Reconstruction-Based Semi-supervised Feature Subspace Learning

Feature subspaces have been widely valued and applied in image classification. However, majority conventional subspace learning approaches are supervised. The data containing only partial labeled samples will lead to unsatisfactory classification results in practical applications. To address the problems, in this paper, we design a graph semi-supervised feature subspace method named low rank sample reconstruction-based semi-supervised feature subspace learning, which combines graph-based semi-supervised learning and low lank sample reconstruction into the same model framework. The proposed method combines the low rank representation constraint into the reconstruction constraint, it then exploits the graph-based label propagation algorithm to predict the unlabeled labels to obtain the labels of all samples. The experimental results demonstrate that the robustness and the classification efficiency of our method.