SIAM Review
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
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
Sparse approximate solutions to semidefinite programs
LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
Distance metric learning with eigenvalue optimization
The Journal of Machine Learning Research
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The success of many machine learning algorithms (e.g. the nearest neighborhood classification and k-means clustering) depends on the representation of the data as elements in a metric space. Learning an appropriate distance metric from data is usually superior to the default Euclidean distance. In this paper, we revisit the original model proposed by Xing et al. [25] and propose a general formulation of learning a Mahalanobis distance from data. We prove that this novel formulation is equivalent to a convex optimization problem over the spectrahedron. Then, a gradient-based optimization algorithm is proposed to obtain the optimal solution which only needs the computation of the largest eigenvalue of a matrix per iteration. Finally, experiments on various UCI datasets and a benchmark face verification dataset called Labeled Faces in the Wild (LFW) demonstrate that the proposed method compares competitively to those state-of-the-art methods.