Semi-supervised Learning with Locally Linear Coordination for Face Recognition

In this paper, we proposed a novel semi-supervised classification method with locally linear coordination for face recognition. The key idea of robust mixture modeling by t-distributions is combined with probabilistic subspace mixture models. It solves the robustness problems of locally linear coordination, by introducing a weighted reformulation of the embedding step. Comparison experiments between the proposed method and the other two methods: PCA and LDA, are performed. The results show that the proposed method achieves the best face recognition.