.png){kind=icon}
返回
A multi-birth metric learning framework based on binary constraints
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2022.07.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-metric learning plays a significant role in improving the generalization of algorithms related to distance metrics since using a single metric is sometimes insufficient to handle complex data. Metric learning can adjust automatically the distance between samples to make the intra-class samples compact while making the inter-class distance as far as possible. To implement this intention better,in this work, we propose a novel multi-metric learning framework based on the pair constraints instead of triple constraints to reduce computational burden. To solve effectively the problem, we first propose a multi-birth metric learning model (termed MBML), where for each class sample, the global metric and a local metric are jointly trained. Both global and local structural information are adapted to better depict sample information. Then two alternating iterative algorithms are developed to optimize the MBML. The convergence of the proposed algorithm and complexity are analyzed theoretically. Moreover, a fast diagonal multi-metric learning method is proposed based on binary constraints, and problem can be reformulated a linear programming, with fast training speed, low the computational burden and the global optimal solutions. Numerical experiments are carried out on different scales and different types of datasets including an artificial data, benchmark datasets and an image database from binary class and multi-class problems. Experiment results confirm the feasibility and effectiveness of the proposed methods. (C) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Multi-metric learning
Pair constraints
Large margin classification
Alternating iteration algorithm
Classifications
期刊
IF:
6.3
论文数:
7.9K
被引数:
3.0W
机构
引用论文
Eliminating environmental and operational effects on structural modal frequency: A comprehensive review消除环境和操作对结构模态频率的影响: 综合评述
OAAI
OAAI分享收藏
分享收藏Multi-view metric learning based on KL-divergence for similarity measurement基于KL-散度的多视图度量学习的相似性度量.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏
查看更多内容