Learning Distance Functions using Equivalence Relations

By Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall. International Conference on Machine Learning, 2003

510 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0029cf474bce70cfab2e944c4be01f99e741f1f4

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Citations in the corpus

Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure (2007, result): “Comparison to other recent methods for learning similarity measures [1, 10, 20] remains to be done.”
Neighbourhood Components Analysis (2004, methodology): “Many approaches exist for linear dimensionality reduction, ranging from purely unsupervised approaches (such as factor analysis, principal components analysis and independent components analysis) to methods which make use of class labels in addition to input features such as linear discriminant anal”
Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation (2014, methodology): “ysis (RCA) [2], and the Discriminative Component Analysis (DCA) which is based on the RCA [16].”
Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics (2011, unknown): “In particular, some of the previous contributions, e.g., [32], [38], [39], require having training Fig.”
Active Semi-Supervision for Pairwise Constrained Clustering (2004, methodology): “Other work with the pairwise constrained clustering model includes learning distance metrics for clustering from pairwise constraints [17, 22, 34].”
A probabilistic framework for semi-supervised clustering (2004, methodology): “Our initial results as well as other recent work on distance learning for clustering [27, 8, 5, 39] suggest that transforming the data space can be highly beneficial for clustering quality.”
Learning a Mahalanobis Metric from Equivalence Constraints (2005, methodology): “, 2002), followed by the work of Bar-Hillel et al. (2003). Recently Bilenko et al.”
Integrating constraints and metric learning in semi-supervised clustering (2004, methodology): “Also, Bar-Hillel et al. (2003) proposed a Redundant Component Analysis (RCA) algorithm that uses only must-link constraints to learn a Mahalanobis metric using convex optimization.”
DIFFRAC: a discriminative and flexible framework for clustering (2007, methodology): “Results are presented in Table 1: on unsupervised problems, K-means and DIFFRAC Table 1: Comparison of K-means, RCA and linear DIFFRAC, using the clustering metric defined in Section 5 (averaged over 10 replications), for linear and " rbf " kernels and various levels of supervi- sion. have similar p”
The Quadratic-Chi Histogram Distance Family (2010, methodology): “We would also like to explore whether metric learning methods such as [31,32,33,34,35,36,37,38] can be generalized for the Quadratic-Chi histogram distance.”
Large Scale Online Learning of Image Similarity Through Ranking (2009, methodology): “We have also experimented with the methods of Xing et al. (2003) and RCA (Bar-Hillel et al., 2003).”
Deep Learning for Content-Based Image Retrieval: A Comprehensive Study (2014, background): “Distance metric learning for image retrieval has been extensively studied in both machine learning and multimedia retrieval communities [12, 2, 48, 29, 15, 47, 33, 46].”
Linear dimensionality reduction: survey, insights, and generalizations (2014, methodology): “Examples include relevant component analysis for DML (Bar-Hillel et al., 2003), neighborhood component analysis (Goldberger et al.”
Online multimodal deep similarity learning with application to image retrieval (2013, methodology): “…we compare the proposed OMDSL algorithm for image retrieval, against 1 http://www.vision.caltech.edu/Image_Datasets/Caltech101/ 2 http://web.mit.edu/torralba/www/indoor.html 3 http://imageclef.org/ several representative distance metric learning algorithms, including RCA [2], LMNN [47], OASIS [8].”