Semi-supervised Neighborhood Preserving Discriminant Embedding: A Semi-supervised Subspace Learning Algorithm

Over the last decade, supervised and unsupervised subspace learning methods, such as LIDA and NPE, have been applied for face recognition. In real life applications, besides unlabeled image data, prior knowledge in the form of labeled data is also available, and can be incorporated in subspace learning algorithm resulting in improved performance. In this paper, we propose a subspace learning method based on semi-supervised neighborhood preserving discriminant learning, which we call Semi-supervised Neighborhood Preserving Discriminant Embedding (SNPDE). The method preserves the local neighborhood structure of face manifold using NPE, and maximizes the separability of different classes using LDA. Experimental results on two face databases demonstrate the effectiveness of the proposed method.