Neural Computation
Local algorithms for pattern recognition and dependencies estimation
Neural Computation
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Metric learning by discriminant neighborhood embedding
Pattern Recognition
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Parametric distance metric learning with label information
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Distance learning in discriminative vector quantization
Neural Computation
Generalized iterative RELIEF for supervised distance metric learning
Pattern Recognition
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
Regularization in matrix relevance learning
IEEE Transactions on Neural Networks
Distance metric learning by minimal distance maximization
Pattern Recognition
Improving SVM classification on imbalanced time series data sets with ghost points
Knowledge and Information Systems
Conscience online learning: an efficient approach for robust kernel-based clustering
Knowledge and Information Systems
A survey of the state of the art in learning the kernels
Knowledge and Information Systems
Sparse transfer learning for interactive video search reranking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A boosting approach for supervised Mahalanobis distance metric learning
Pattern Recognition
MatchSim: a novel similarity measure based on maximum neighborhood matching
Knowledge and Information Systems
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The ultimate goal of distance metric learning is to incorporate abundant discriminative information to keep all data samples in the same class close and those from different classes separated. Local distance metric methods can preserve discriminative information by considering the neighborhood influence. In this paper, we propose a new local discriminative distance metrics (LDDM) algorithm to learn multiple distance metrics from each training sample (a focal sample) and in the vicinity of that focal sample (focal vicinity), to optimize local compactness and local separability. Those locally learned distance metrics are used to build local classifiers which are aligned in a probabilistic framework via ensemble learning. Theoretical analysis proves the convergence rate bound, the generalization bound of the local distance metrics and the final ensemble classifier. We extensively evaluate LDDM using synthetic datasets and large benchmark UCI datasets.