Discriminant Adaptive Nearest Neighbor Classification
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Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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Learning More Accurate Metrics for Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Adaptive Kernel Metric Nearest Neighbor Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)
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Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis
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Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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Manifold-ranking based image retrieval
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Learning an image manifold for retrieval
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Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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Semi-Supervised Clustering with Metric Learning Using Relative Comparisons
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning semantics-preserving distance metrics for clustering graphical data
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A transductive framework of distance metric learning by spectral dimensionality reduction
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On the value of pairwise constraints in classification and consistency
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Nonlinear adaptive distance metric learning for clustering
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AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Choosing a distance metric for automatic word categorization
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IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
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Effective diagnosis of Alzheimer's disease by means of distance metric learning and random forest
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Effective diagnosis of alzheimer's disease by means of distance metric learning
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The Trace Ratio Optimization Problem for Dimensionality Reduction
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Learning from pairwise constraints by Similarity Neural Networks
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Fast neighborhood component analysis
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Pattern Recognition
Twin Mahalanobis distance-based support vector machines for pattern recognition
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Robust principal component analysis with non-greedy l1-norm maximization
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Semi-supervised fuzzy clustering with metric learning and entropy regularization
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International Journal of Computer Vision and Image Processing
An elastic net clustering algorithm for non-linearly separable data
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Classification of opinion questions
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Local discriminative distance metrics ensemble learning
Pattern Recognition
Probabilistic non-linear distance metric learning for constrained clustering
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Pattern Recognition
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Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links. As one kind of side information, a must-link indicates the pair of the two data points must be in a same class, while a cannot-link indicates that the two data points must be in two different classes. Given must-link and cannot-link information, our goal is to learn a Mahalanobis distance metric. Under this metric, we hope the distances of point pairs in must-links are as small as possible and those of point pairs in cannot-links are as large as possible. This task is formulated as a constrained optimization problem, in which the global optimum can be obtained effectively and efficiently. Finally, some applications in data clustering, interactive natural image segmentation and face pose estimation are given in this paper. Experimental results illustrate the effectiveness of our algorithm.