Results 31 to 40 of about 7,006,651 (299)
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.
Heitz, Matthieu +4 more
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Proceedings of the 26th International Conference on World Wide Web, 2017 Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users' preferences but also the user-user and item-item ...
Cheng-Kang Hsieh +5 more
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Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The rapid development of deep learning brings effective solutions for remote sensing image interpretation. Training deep neural network models usually require a large number of manually labeled samples. However, there is a limitation to obtain sufficient
Xian Sun +5 more
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J. Mach. Learn. Res., 2011 Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in practice when this parameter is ...
Marco Cuturi, David Avis
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Unsupervised Metric Learning with Synthetic Examples [PDF]
, 2020 Distance Metric Learning (DML) involves learning an embedding that brings similar examples closer while moving away dissimilar ones. Existing DML approaches make use of class labels to generate constraints for metric learning.
Dutta, Ujjal Kr +3 more
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Distributed Semi-Supervised Metric Learning
IEEE Access, 2016 Over the last decade, many pairwise-constraint-based metric learning algorithms have been developed to automatically learn application-specific metrics from data under similarity/dissimilarity data-pair constraints (weak labels).
Pengcheng Shen, Xin Du, Chunguang Li
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CoRR, 2017 The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider 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 previous experiences and knowledge.
Gan Sun +3 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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CoRR, 2019 Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated than in Euclidean space, and thus many techniques for processing and analysis taken for granted in Euclidean ...
Max Aalto, Nakul Verma
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Query-dependent metric learning for adaptive, content-based image browsing and retrieval [PDF]
, 2014 Content-based image retrieval (CBIR) systems often incorporate a relevance feedback mechanism in which retrieval is adapted based on users identifying images as relevant or irrelevant.
Han, Junwei, McKenna, Stephen; id_orcid
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