Results 11 to 20 of about 826,117 (259)
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
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
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
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
REID (Research and Evaluation in Education), 2021 Nowadays, in the pandemic of COVID-19, e-learning systems have been widely used to facilitate teaching and learning processes between lecturers and students.
Sulis Sandiwarno
doaj +1 more source
Deep Transfer Metric Learning [PDF]
IEEE Transactions on Image Processing, 2016 Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep
Junlin Hu +3 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
openaire +4 more sources
Query-Augmented Active Metric Learning [PDF]
Journal of the American Statistical Association, 2022 In this paper we propose an active metric learning method for clustering with pairwise constraints. The proposed method actively queries the label of informative instance pairs, while estimating underlying metrics by incorporating unlabeled instance pairs, which leads to a more accurate and efficient clustering process.
Deng, Yujia +3 more
openaire +2 more sources