Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fundamentals of matrix computations
Fundamentals of matrix computations
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting
Engineering Applications of Artificial Intelligence
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Over the last decade, supervised and unsupervised subspace learning methods, such as LDA 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.