Adaptive Graph Constrained NMF for Semi-Supervised Learning

Recently, graph-based semi-supervised learning (GB-SSL) has received a lot of attentions in pattern recognition, computer vision and information retrieval. The key parts of GB-SSL are designing loss function and constructing graph. In this paper, we proposed a new semi-supervised learning method where the loss function is modeled via graph constrained non-negative matrix factorization (GCNMF). The model can effectively cooperate the precious label information and the local consistency among samples including labeled and unlabeled data. Meanwhile, an adaptive graph construction method is presented so that the selected neighbors of one sample are as similar as possible, which makes the local consistency be correctly preserved in the graph. The experimental results on real world data sets including object image, face and handwritten digit have shown the superiority of our proposed method.