JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2022, Vol. 57 ›› Issue (4): 12-20.doi: 10.6040/j.issn.1671-9352.7.2021.149

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Multi-metric learning algorithm based on constraint hierarchical weighting

HAN Lu1, GUO Xin-yao1, WEI Wei1,2, LIANG Ji-ye1,2*   

  1. 1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China;
    2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, Shanxi, China
  • Published:2022-03-29

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Abstract: In order to solve the problem of insufficient local metric fitting ability caused by inaccurate sample partitioning when the classic multi-metric learning algorithm learns metrics from sample partitions obtained in advance, based on the idea of constraint stratification weighting, this paper proposes to assign metrics to constraints layer by layer and makes the measurement as far as possible to meet the optimization model of all constraints, while adding regular terms to make the constraints corresponding to different metrics should be as different as possible. Since different constraints formed by a single sample may correspond to different local metrics, compared with traditional multi-metric learning methods, the proposed algorithm can obtain finer local metrics and is more flexible, making the metric's fitting ability stronger. Experimental results show that the proposed algorithm has obvious advantages compared with representative single-metric learning algorithms and multi-metric learning algorithms on real data sets.

Key words: metric learning, constraint weighting, Mahalanobis distance, triplet constraint, multi-metric learning

CLC Number: 

  • TP181
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