Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
PageRank, HITS and a unified framework for link analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A Graph Based Subspace Semi-supervised Learning Framework for Dimensionality Reduction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directed graph learning via high-order co-linkage analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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Most existing graph-based semi-supervised classification methods use pairwise similarities as edge weights of an undirected graph with images as the nodes of the graph. Recently several new graph construction methods produce, however, directed graph (asymmetric similarity between nodes). A simple symmetrization is often used to convert a directed graph to an undirected one. This, however, loses important structural information conveyed by asymmetric similarities. In this paper, we propose a novel symmetric co-linkage similarity which captures the essential relationship among the nodes in the directed graph.We apply this new co-linkage similarity in two important computer vision tasks for image categorization: object recognition and image annotation. Extensive empirical studies demonstrate the effectiveness of our method.