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Describing movement learning using metric learning [PDF]
PLoS ONE, 2023 Analysing movement learning can rely on human evaluation, e.g. annotating video recordings, or on computing means in applying metrics on behavioural data.
Antoine Loriette +3 more
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Distance-Metric Learning for Personalized Survival Analysis [PDF]
Entropy, 2023 Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to
Wolfgang Galetzka +4 more
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Multiple Cayley-Klein metric learning. [PDF]
PLoS ONE, 2017 As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks.
Yanhong Bi, Bin Fan, Fuchao Wu
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Feature Fusion and Metric Learning Network for Zero-Shot Sketch-Based Image Retrieval [PDF]
Entropy, 2023 Zero-shot sketch-based image retrieval (ZS-SBIR) is an important computer vision problem. The image category in the test phase is a new category that was not visible in the training stage.
Honggang Zhao, Mingyue Liu, Mingyong Li
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Research on the Few-Shot Learning Based on Metrics [PDF]
SHS Web of Conferences, 2022 Deep learning has been rapidly developed and obtained great achievements with a dataintensive condition. However, sufficient datasets are not always available in practical application. In the absence of data, humans can still perform well in studying and
Shen Yican
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Geometric Metric Learning for Multi-Output Learning
Mathematics, 2022 Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and more attractive.
Huiping Gao, Zhongchen Ma
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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
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Frontiers in Human Neuroscience, 2022 Decoding movement related intentions is a key step to implement BMIs. Decoding EEG has been challenging due to its low spatial resolution and signal to noise ratio.
William Plucknett +2 more
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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
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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
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