Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Semi-Supervised Learning
Content-based image retrieval with relevance feedback using random walks
Pattern Recognition
A Random Walk Procedure for Texture Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative sparse coding on multi-manifolds
Knowledge-Based Systems
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The semi-supervised learning paradigm allows that a large amount of unlabeled data be classified using just a few labeled data. To account for the minimal a priori label knowledge, the information provided by the unlabeled data is also used in the classification process. This paper describes a semi-supervised technique that uses random walk limiting probabilities to propagate label information. Each label is propagated through a network of unlabeled instances via a biased random walk. The probability of a vertex receiving a label is expressed in terms of the limiting conditions of the walk process. Simulations show that the proposed technique is competitive with benchmarked techniques.