IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in Statistical Pattern Recognition Using Tangent Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Discovery in Non-Metric Pairwise Data
The Journal of Machine Learning Research
Learning with non-metric proximity matrices
Proceedings of the 13th annual ACM international conference on Multimedia
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Semi-supervised learning for multi-component data classification
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Face recognition under occlusions and variant expressions with partial similarity
IEEE Transactions on Information Forensics and Security
Ranking-based classification of heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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In many applications non-metric distances are better than metric distances in reflecting the perceptual distances of human beings. Previous studies on non-metric distances mainly focused on supervised setting and did not consider the usefulness of unlabeled data. In this paper, we present probably the first study of label propagation on graphs induced from non-metric distances. The challenge here lies in the fact that the triangular inequality does not hold for non-metric distances and therefore, a direct application of existing label propagation methods will lead to inconsistency and conflict. We show that by applying spectrum transformation, non-metric distances can be converted into metric ones, and thus label propagation can be executed. Such methods, however, suffer from the change of original semantic relations. As a main result of this paper, we prove that any nonmetric distance matrix can be decomposed into two metric distance matrices containing different information of the data. Based on this recognition, our proposed NMLP method derives two graphs from the original non-metric distance and performs a joint label propagation on the joint graph. Experiments validate the effectiveness of the proposed NMLP method.