Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to combine distances for complex representations
Proceedings of the 24th international conference on Machine learning
Journal of Cognitive Neuroscience
Distance Learning for Similarity Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vector Method With Kernels
IEEE Transactions on Neural Networks
A Random Extension for Discriminative Dimensionality Reduction and Metric Learning
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
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Maximally Collapsing Metric Learning is a recently proposed algorithm to estimate a metric matrix from labelled data. The purpose of this work is to extend this approach by considering a set of landmark points which can in principle reduce the cost per iteration in one order of magnitude. The proposal is in fact a generalized version of the original algorithm that can be applied to larger amounts of higher dimensional data. Exhaustive experimentation shows that very similar behavior at a lower cost is obtained for a wide range of the number of landmark points used.