Results 21 to 30 of about 762,608 (309)
Kernel Geometric Mean Metric Learning
Applied Sciences, 2023 Geometric mean metric learning (GMML) algorithm is a novel metric learning approach proposed recently. It has many advantages such as unconstrained convex objective function, closed form solution, faster computational speed, and interpretability over ...
Zixin Feng +4 more
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Learning Neighborhoods for Metric Learning [PDF]
, 2012 Metric learning methods have been shown to perform well on different learning tasks. Many of them rely on target neighborhood relationships that are computed in the original feature space and remain fixed throughout learning. As a result, the learned metric reflects the original neighborhood relations.
Wang Jun +2 more
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Parameter-free basis allocation for efficient multiple metric learning
Machine Learning: Science and Technology, 2023 Metric learning involves learning a metric function for distance measurement, which plays an important role in improving the performance of classification or similarity-based algorithms.
Dongyeon Kim +3 more
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Metric Learning for Individual Fairness [PDF]
, 2020 There has been much discussion concerning how "fairness" should be measured or enforced in classification. Individual Fairness [Dwork et al., 2012], which requires that similar individuals be treated similarly, is a highly appealing definition as it ...
Ilvento, Christina
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Object Tracking With Structured Metric Learning
IEEE Access, 2019 In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning.
Xiaolin Zhao +4 more
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Learning similarity metric with SVM [PDF]
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 In this paper, we show how to learn a good similarity metric for SVM classification. We present a novel approach to simultaneously learn a Mahalanobis similarity metric and an SVM classifier. Different from previous approaches, we optimize the Mahalanobis metric directly for minimizing the SVM classification error.
Xiaoqiang Zhu, +6 more
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Fantope Regularization in Metric Learning [PDF]
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014 This paper introduces a regularization method to ex-plicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning. To this end, we propose to incorporate in the objective function a linear regularization term that minimizes the k smallest eigenvalues of the distance matrix.
Law, Marc +2 more
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Gong-kuang zidonghua, 2023 In the traditional personnel re-recognition method in coal mine underground based on metric learning, because metric learning ignores the absolute distance between positive and negative samples, the gradient of the loss function disappears or disperses ...
ZHANG Liya, WANG Yu, HAO Bonan
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Similarity Metric Learning [PDF]
, 2012 Similarity metric learning models the general semantic similarities and distances between objects and classes of objects (e.g. persons) in order to recognise them. Different strategies and models based on Deep Learning exist and generally consist in learning a non-linear projection into a lower dimensional vector space where the semantic similarity ...
Duffner, Stefan +3 more
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A review on multi-task metric learning
Big Data Analytics, 2018 Distance metric plays an important role in machine learning which is crucial to the performance of a range of algorithms. Metric learning, which refers to learning a proper distance metric for a particular task, has attracted much attention in machine ...
Peipei Yang, Kaizhu Huang, Amir Hussain
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