Regularized Distance Metric Learning: Theory and Algorithm

By Rong Jin, Shijun Wang, Yang Zhou. 2009

208 citations recorded by the source corpus.

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Citations in the corpus

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions (2016, neutral): “We consider the family of Mahalanobis distances following criterion (Jin et al., 2009): where + and 0 otherwise.”
A Survey on Metric Learning for Feature Vectors and Structured Data (2013, methodology): “RDML (Jin et al.)”
Online Deep Metric Learning (2018, methodology): “In order to verify the extendibility of the proposed framework, we take the other three OML algorithms (e.g., LEGO, RDML and OPML) as the base OML layer and construct their corresponding deep versions, respectively (i.e., LEGO-Deep, RDML-Deep and OPML-Deep).”
Online Learning: A Comprehensive Survey (2018, methodology): “Another similar work named Online Regularized Metric Learning (Jin et al., 2009) is simpler due to the adoption of fixed threshold, and adopts the following loss function `t(A) = max(0, b− yt(1− dA(x1t ,x2t ))) whose gradient is ∇`t(A) = yt(x1t − x2t )(x1t − x2t )> At time t, if the prediction is in”
Robustness and generalization for metric learning (2012, background): “✷ Note that for the special case of Example 1, a generalization bound (with same order of convergence rate) based on uniform stability was derived in [14].”
Deep Learning for Content-Based Image Retrieval: A Comprehensive Study (2014, methodology): “Unlike the batch learning methods, in order to handle large-scale data, online DML algorithms have been actively studied recently [22, 24].”
Online multimodal deep similarity learning with application to image retrieval (2013, background): “In terms of learning methodology, most existing DML studies generally follow batch machine learning methods, except that some recent DML studies begin to explore online learning techniques [19, 21].”
Decomposition-Based Transfer Distance Metric Learning for Image Classification (2014, methodology): “A popular categorization of the DML methods is: supervised DML [1], [3], [9], [10], [12], [18] and unsupervised DML [19], [20], according to the underlying learning paradigm.”
A Theoretical Analysis of Metric Hypothesis Transfer Learning (2015, methodology): “Building upon the framework of stability proposed in (Bousquet & Elisseeff, 2002), (Jin et al., 2009) propose one of the first study of the generalization ability of a metric learning algorithm.”
Metric Learning: A Survey (2013, background): “We refer the reader to [9, 40] for further details.”