Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
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
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Large margin nearest neighbor classifiers
IEEE Transactions on Neural Networks
An online metric learning approach through margin maximization
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Improved support vector machines with distance metric learning
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Random forests for metric learning with implicit pairwise position dependence
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning neighborhoods for metric learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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In this paper, we address the metric learning problem utilizing a margin-based approach. Our metric learning problem is formulated as a quadratic semi-definite programming problem (QSDP) with local neighborhood constraints, which is based on the Support Vector Machine (SVM) framework. The local neighborhood constraints ensure that examples of the same class are separated from examples of different classes by a margin. In addition to providing an efficient algorithm to solve the metric learning problem, extensive experiments on various data sets show that our algorithm is able to produce a new distance metric to improve the performance of the classical K-nearest neighbor (KNN) algorithm on the classification task. Our performance is always competitive and often significantly better than other state-of-the-art metric learning algorithms.