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ICML
2007
IEEE

Neighbor search with global geometry: a minimax message passing algorithm

10 years 28 days ago
Neighbor search with global geometry: a minimax message passing algorithm
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...
Kye-Hyeon Kim, Seungjin Choi
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Kye-Hyeon Kim, Seungjin Choi
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