Results 41 to 50 of about 210,501 (299)
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
Jason V. Davis +4 more
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Dealing with non-metric dissimilarities in fuzzy central clustering algorithms [PDF]
, 2008 Clustering is the problem of grouping objects on the basis of a similarity measure among them. Relational clustering methods can be employed when a feature-based representation of the objects is not available, and their description is given in terms of ...
Filippone, Maurizio +2 more
core +1 more source
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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Attribute-enhanced metric learning for face retrieval
EURASIP Journal on Image and Video Processing, 2018 Metric learning is a significant factor for media retrieval. In this paper, we propose an attribute label enhanced metric learning model to assist face image retrieval.
Yuchun Fang, Qiulong Yuan
doaj +1 more source
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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LM-Metric: Learned Pair Weighting and Contextual Memory for Deep Metric Learning
Pattern Recognition, 2023 <p>Learned Pair Weighting and Contextual Memory for Deep Metric Learning. </p>
Shiyang Yan +4 more
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Adversarial Similarity Metric Learning for Kinship Verification
IEEE Access, 2019 Given a pair of facial images, it is an interesting yet challenging problem to determine if there is a kin relation between them. Recent research on that topic has made encouraging progress by learning a kin similarity metric from kinship data.
Zeqiang Wei +4 more
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Adaptive Multi-Proxy for Remote Sensing Image Retrieval
Remote Sensing, 2022 With the development of remote sensing technology, content-based remote sensing image retrieval has become a research hotspot. Remote sensing image datasets not only contain rich location, semantic and scale information but also have large intra-class ...
Xinyue Li +4 more
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Unsupervised natural image patch learning
Computational Visual Media, 2019 A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity.
Dov Danon +3 more
doaj +1 more source