A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Robust locally linear embedding
Pattern Recognition
Semi-supervised learning based on nearest neighbor rule and cut edges
Knowledge-Based Systems
Robust semi-supervised learning for biometrics
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Sparse regularization for semi-supervised classification
Pattern Recognition
An improved spectral clustering algorithm based on random walk
Frontiers of Computer Science in China
Semi-supervised image classification based on sparse coding spatial pyramid matching
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Semi-supervised object recognition based on Connected Image Transformations
Expert Systems with Applications: An International Journal
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We investigate the issue of graph-based semi-supervised learning (SSL). The labeled and unlabeled data points are represented as vertices in an undirected weighted neighborhood graph, with the edge weights encoding the pairwise similarities between data objects in the same neighborhood. The SSL problem can be then formulated as a regularization problem on this graph. In this paper we propose a robust self-tuning graph-based SSL method, which (1) can determine the similarities between pairwise data points automatically; (2) is not sensitive to outliers. Promising experimental results are given for both synthetic and real data sets.