Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Semi-Supervised Classification Using Linear Neighborhood Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Linear Neighborhood Propagation and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlative linear neighborhood propagation for video annotation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Semi-Supervised Learning
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In this paper, a novel semi-supervised learning approach is proposed. It assumes that, for the ith sample x"i, the samples from x"i's sparse neighborhood have the same label with x"i and the label of x"i can be linearly reconstructed by the labels of those samples from x"i's sparse neighborhood. Our algorithm firstly selects the sparse neighborhood for each sample, and then in that sparse neighborhood finds the sparse coefficients to represent the local geometry structure, finally seeks a label propagation way. Different from many existing methods, we construct the adapting graph, simultaneously, give the weight of each edge. What's more, we highlight the role of those samples in that sparse neighborhood, meanwhile, eliminate the role of those samples out of that sparse neighborhood. The experimental results on face recognition and document classification demonstrate the effectiveness and efficiency of our proposed approach in this paper.