Introduction to statistical pattern recognition (2nd ed.)
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Elements of information theory
Elements of information theory
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Wrappers for feature subset selection
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Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
The Journal of Machine Learning Research
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
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Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Learning a kernel function for classification with small training samples
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability of Unstable Learning Algorithms
Machine Learning
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised metric learning by maximizing constraint margin
Proceedings of the 17th ACM conference on Information and knowledge management
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized iterative RELIEF for supervised distance metric learning
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Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimal Global Nearest Neighbor Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parameter-Free Framework for General Supervised Subspace Learning
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Image Processing
Local discriminative distance metrics ensemble learning
Pattern Recognition
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Determining a proper distance metric is often a crucial step for machine learning. In this paper, a boosting algorithm is proposed to learn a Mahalanobis distance metric. Similar to most boosting algorithms, the proposed algorithm improves a loss function iteratively. In particular, the loss function is defined in terms of hypothesis margins, and a metric matrix base-learner specific to the boosting framework is also proposed. Experimental results show that the proposed approach can yield effective Mahalanobis distance metrics for a variety of data sets, and demonstrate the feasibility of the proposed approach.