Shape quantization and recognition with randomized trees
Neural Computation
Machine Learning
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Metric Learning for Text Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
Metric Learning: A Support Vector Approach
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Learning instance specific distances using metric propagation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Text document clustering with metric learning
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Scalable large-margin Mahalanobis distance metric learning
IEEE Transactions on Neural Networks
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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Metric learning makes it plausible to learn semantically meaningful distances for complex distributions of data using label or pairwise constraint information. However, to date, most metric learning methods are based on a single Mahalanobis metric, which cannot handle heterogeneous data well. Those that learn multiple metrics throughout the feature space have demonstrated superior accuracy, but at a severe cost to computational efficiency. Here, we adopt a new angle on the metric learning problem and learn a single metric that is able to implicitly adapt its distance function throughout the feature space. This metric adaptation is accomplished by using a random forest-based classifier to underpin the distance function and incorporate both absolute pairwise position and standard relative position into the representation. We have implemented and tested our method against state of the art global and multi-metric methods on a variety of data sets. Overall, the proposed method outperforms both types of method in terms of accuracy (consistently ranked first) and is an order of magnitude faster than state of the art multi-metric methods (16x faster in the worst case).