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
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating frontal view face image for pose invariant face recognition
Pattern Recognition Letters
Boosted discriminant projections for nearest neighbor classification
Pattern Recognition
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction methods for real-time face detection and classification
EURASIP Journal on Applied Signal Processing
A feature extraction approach based on typical samples and its application to face recognition
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Metric learning by discriminant neighborhood embedding
Pattern Recognition
Extracting the optimal dimensionality for local tensor discriminant analysis
Pattern Recognition
A decision-boundary-oriented feature selection method and its application to face recognition
Pattern Recognition Letters
Affine-invariant contours recognition using an incremental hybrid learning approach
Pattern Recognition Letters
Stepwise nearest neighbor discriminant analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Boosted online learning for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A feature extraction approach based on typical samples and its application to face recognition
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Registration and retrieval of highly elastic bodies using contextual information
Pattern Recognition Letters
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
An adaptive nonparametric discriminant analysis method and its application to face recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Generalized re-weighting local sampling mean discriminant analysis
Pattern Recognition
Push-Pull marginal discriminant analysis for feature extraction
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Dimensionality reduction by minimizing nearest-neighbor classification error
Pattern Recognition Letters
Robust linearly optimized discriminant analysis
Neurocomputing
Margin maximizing discriminant analysis for multi-shot based object recognition
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
The contribution of external features to face recognition
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Learning discriminative canonical correlations for object recognition with image sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Non-parametric Fisher's discriminant analysis with kernels for data classification
Pattern Recognition Letters
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Nonparametric discriminant analysis (NDA), opposite to other nonparametric techniques, has received little or no attention within the pattern recognition community. Nearest neighbor classification (NN) instead, has a well established position among other classification techniques due to its practical and theoretical properties. In this paper, we observe that when we seek a linear representation adapted to improve NN performance, what we obtain not surprisingly is quite close to NDA. Since a hierarchy is provided on the extracted features it also serves as a dimensionality reduction technique that preserves NN performance. Experiments evaluate and compare NN classification using our proposed representation against more classical feature extraction techniques.