Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-Based Semisupervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph construction and b-matching for semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Graph-based semi-supervised learning as a generative model
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semi-Supervised Learning
Nonnegative sparse coding for discriminative semi-supervised learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Non-negative low rank and sparse graph for semi-supervised learning
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Fixed-rank representation for unsupervised visual learning
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Robust Recovery of Subspace Structures by Low-Rank Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Graph based semi-supervised learning (GSSL) plays an important role in machine learning systems. The most crucial step in GSSL is graph construction. Although several interesting graph construction methods have been proposed in recent years, how to construct an effective graph is still an open problem. In this paper, we develop a novel approach to constructing graph, which is based on low-rank coding and b-matching constraint. By virtue of recent advances in low-rank subspace recovery theory, compact encoding using low-rank representation coefficients allows us to obtain a robust similarity metric between all pairs of samples. Meanwhile, the b-matching constraint helps in obtaining a sparse and balanced graph, which benefits label propagation in GSSL. We build a joint optimization model to learn low-rank codes and balanced graph simultaneously. After using a graph re-weighting strategy, we present a semi-supervised learning algorithm by incorporating our sparse and balanced graph with Gaussian harmonic function (GHF). Experimental results on the Extended YaleB, PIE, ORL and USPS databases demonstrate that our graph outperforms several state-of-the-art graphs, especially when the labeled samples are very scarce.