Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Locally linear metric adaptation for semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
ICML '05 Proceedings of the 22nd international conference on Machine learning
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Characterizing pathological deviations from normality using constrained manifold-learning
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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Neighbor search is a fundamental task in machine learning, especially in classification and retrieval. Efficient nearest neighbor search methods have been widely studied, with their emphasis on data structures but most of them did not consider the underlying global geometry of a data set. Recent graph-based semi-supervised learning methods capture the global geometry, but suffer from scalability and parameter tuning problems. In this paper we present a (nearest) neighbor search method where the underlying global geometry is incorporated and the parameter tuning is not required. To this end, we introduce deterministic walks as a deterministic counterpart of Markov random walks, leading us to use the minimax distance as a global dissimilarity measure. Then we develop a message passing algorithm for efficient minimax distance calculation, which scales linearly in both time and space. Empirical study reveals the useful behavior of the method in image retrieval and semi-supervised learning.