GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Convex Optimization
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Kernel-Based Transductive Learning with Nearest Neighbors
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Some new directions in graph-based semi-supervised learning
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Collection-based sparse label propagation and its application on social group suggestion from photos
ACM Transactions on Intelligent Systems and Technology (TIST)
Graph transduction as a non-cooperative game
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Active learning for node classification in assortative and disassortative networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph transduction as a noncooperative game
Neural Computation
Manifold-Regularized minimax probability machine
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Semi-supervised learning with nuclear norm regularization
Pattern Recognition
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
Laplacian minimax probability machine
Pattern Recognition Letters
Single network relational transductive learning
Journal of Artificial Intelligence Research
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Recent studies have shown that graph-based approaches are effective for semi-supervised learning. The key idea behind many graph-based approaches is to enforce the consistency between the class assignment of unlabeled examples and the pairwise similarity between examples. One major limitation with most graph-based approaches is that they are unable to explore dissimilarity or negative similarity. This is because the dissimilar relation is not transitive, and therefore is difficult to be propagated. Furthermore, negative similarity could result in unbounded energy functions, which makes most graph-based algorithms unapplicable. In this paper, we propose a new graph-based approach, termed as "mixed label propagation" which is able to effectively explore both similarity and dissimilarity simultaneously. In particular, the new framework determines the assignment of class labels by (1) minimizing the energy function associated with positive similarity, and (2) maximizing the energy function associated with negative similarity. Our empirical study with collaborative filtering shows promising performance of the proposed approach.