Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric discriminant analysis and nearest neighbor classification
Pattern Recognition Letters
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Improving nearest neighbor classification with cam weighted distance
Pattern Recognition
Discriminant neighborhood embedding for classification
Pattern Recognition
Parametric distance metric learning with label information
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Stepwise nearest neighbor discriminant analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large margin nearest neighbor classifiers
IEEE Transactions on Neural Networks
Pattern Recognition Letters
Fast neighborhood component analysis
Neurocomputing
Local discriminative distance metrics ensemble learning
Pattern Recognition
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In this paper, we learn a distance metric in favor of classification task from available labeled samples. Multi-class data points are supposed to be pulled or pushed by discriminant neighbors. We define a discriminant adjacent matrix in favor of classification task and learn a map transforming input data into a new space such that intra-class neighbors become even more nearby while extra-class neighbors become as far away from each other as possible. Our method is non-parametric, non-iterative, and immune to small sample size (SSS) problem. Target dimensionality of the new space is selected by spectral analysis in the proposed method. Experiments on real-world data sets demonstrate the effectiveness of our method.