Results 11 to 20 of about 822,037 (305)
Two-Stage Metric Learning [PDF]
, 2014 In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points.
Kalousis, Alexandros +4 more
core +4 more sources
Ground Metric Learning on Graphs [PDF]
Journal of Mathematical Imaging and Vision, 2020 Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications strongly hinges on whether that ground metric parameter is suitably chosen.
Matthieu Heitz +4 more
openalex +5 more sources
Adaptive Neighborhood Metric Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining. Since the inseparable samples are often mixed with hard samples, current informative sample mining strategies used to deal with inseparable problem may bring up some side-effects, such as instability of objective function ...
Song, Kun +4 more
openaire +3 more sources
Lifelong Metric Learning [PDF]
IEEE Transactions on Cybernetics, 2019 The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider a lifelong learning problem to mimic "human learning," i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating the previous experiences.
Gan Sun +5 more
openaire +2 more sources
A Local-to-Global Metric Learning Framework From the Geometric Insight
IEEE Access, 2020 Metric plays a key role in the description of similarity between samples. An appropriate metric for data can well represent their distribution and further promote the performance of learning tasks.
Yaxin Peng +3 more
doaj +1 more source
Deep Multiple Metric Learning for Time Series Classification
IEEE Access, 2021 Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification ...
Zhi Chen +6 more
doaj +1 more source
Information-theoretic metric learning [PDF]
Proceedings of the 24th international conference on Machine learning, 2007 In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem---that of minimizing
Davis, J. +4 more
openaire +2 more sources
Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method
Shock and Vibration, 2021 A novel fault diagnosis method of rolling bearing based on deep metric learning and Yu norm is proposed in this paper, which is called a deep metric learning method based on Yu norm (DMN-Yu).
Zengbing Xu +4 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source