Li Peihua   

Supervisor of Doctorate Candidates
Supervisor of Master's Candidates

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Paper Publications

Title of Paper:Shrinkage Expansion Adaptive Metric Learning


Date of Publication:2014-09-06

Included Journals:EI、CPCI-S、Scopus


Issue:PART 7

Page Number:456-471

Key Words:Shrinkage-expansion rule; adaptive bound constraints; pairwise constrained metric learning; face verification

Abstract:Conventional pairwise constrained metric learning methods usually restrict the distance between samples of a similar pair to be lower than a fixed upper bound, and the distance between samples of a dissimilar pair higher than a fixed lower bound. Such fixed bound based constraints, however, may not work well when the intra-and inter-class variations are complex. In this paper, we propose a shrinkage expansion adaptive metric learning (SEAML) method by defining a novel shrinkage-expansion rule for adaptive pairwise constraints. SEAML is very effective in learning metrics from data with complex distributions. Meanwhile, it also suggests a new rule to assess the similarity between a pair of samples based on whether their distance is shrunk or expanded after metric learning. Our extensive experimental results demonstrated that SEAML achieves better performance than state-of-the-art metric learning methods. In addition, the proposed shrinkage-expansion adaptive pairwise constraints can be readily applied to many other pairwise constrained metric learning algorithms, and boost significantly their performance in applications such as face verification on LFW and PubFig databases.

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