What Makes Objects Similar: A Unified Multi-Metric Learning Approach

2019 ◽  
Vol 41 (5) ◽  
pp. 1257-1270 ◽  
Author(s):  
Han-Jia Ye ◽  
De-Chuan Zhan ◽  
Yuan Jiang ◽  
Zhi-Hua Zhou
Keyword(s):  
Metric Learning ◽  
Learning Approach

scholarly journals An Improved Metric Learning Approach for Degraded Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guofeng Zou ◽  
Yuanyuan Zhang ◽  
Kejun Wang ◽  
Shuming Jiang ◽  
Huisong Wan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Metric Learning ◽  
Computing Time ◽  
Feature Space ◽  
Projection Algorithm ◽  
Learning Approach ◽  
Learning Method ◽  
Matching Problem ◽  
Locality Preserving Projection ◽  
Locality Preserving

To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on supervised locality preserving projection is proposed. In kernel coupled metric learning approach, two elements of different collections are mapped to the unified high dimensional feature space by kernel function, and then generalized metric learning is performed in this space. Experiments based on Yale and CAS-PEAL-R1 face databases demonstrate that the proposed kernel coupled approach performs better in low-resolution and fuzzy face recognition and can reduce the computing time; it is an effective metric method.

scholarly journals Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps

2017 ◽  
Author(s):  
Han-Jia Ye ◽  
De-Chuan Zhan ◽  
Xue-Min Si ◽  
Yuan Jiang
Keyword(s):  
Mahalanobis Distance ◽  
Side Information ◽  
Metric Learning ◽  
Learning Approach ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Information Utilization ◽  
Learning Methods ◽  
Feature Weights ◽  
Distance Computation

Mahalanobis distance metric takes feature weights and correlation into account in the distance computation, which can improve the performance of many similarity/dissimilarity based methods, such as kNN. Most existing distance metric learning methods obtain metric based on the raw features and side information but neglect the reliability of them. Noises or disturbances on instances will make changes on their relationships, so as to affect the learned metric.In this paper, we claim that considering disturbance of instances may help the distance metric learning approach get a robust metric, and propose the Distance metRIc learning Facilitated by disTurbances (DRIFT) approach. In DRIFT, the noise or the disturbance of each instance is learned. Therefore, the distance between each pair of (noisy) instances can be better estimated, which facilitates side information utilization and metric learning.Experiments on prediction and visualization clearly indicate the effectiveness of the proposed approach.

scholarly journals How to Find a Perfect Data Scientist: A Distance-Metric Learning Approach

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 60380-60395 ◽  
Author(s):  
Han Hu ◽  
Yong Luo ◽  
Yonggang Wen ◽  
Yew-Soon Ong ◽  
Xinwen Zhang
Keyword(s):  
Metric Learning ◽  
Learning Approach ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Data Scientist

An improved biometrics technique based on metric learning approach

2012 ◽  
Vol 97 ◽  
pp. 44-51 ◽  
Author(s):  
Xianye Ben ◽  
Weixiao Meng ◽  
Rui Yan ◽  
Kejun Wang
Keyword(s):  
Metric Learning ◽  
Learning Approach

A deep metric learning approach for histopathological image retrieval

Methods ◽  
2020 ◽  
Vol 179 ◽  
pp. 14-25 ◽  
Author(s):  
Pengshuai Yang ◽  
Yupeng Zhai ◽  
Lin Li ◽  
Hairong Lv ◽  
Jigang Wang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Metric Learning ◽  
Learning Approach ◽  
Histopathological Image ◽  
Deep Metric Learning
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