From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis

Authors:
Jian Yang;Lei Zhang;Jing-yu Yang;David Zhang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, PR China;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, PR China;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong
Venue:
Pattern Recognition
Year:
2011

Citing 29
Cited 6

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
On Image Analysis by Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval

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
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Nonparametric discriminant analysis and nearest neighbor classification

Pattern Recognition Letters
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Discriminative Common Vectors for Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Linear Discriminant Analysis for Multimodally Distributed Classes for Face Recognition with a Single Model Image

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
A local mean-based nonparametric classifier

Pattern Recognition Letters
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Novel Adaptive Nearest Neighbor Classifiers Based On Hit-Distance

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rectified nearest feature line segment for pattern classification

Pattern Recognition
Bayes Optimality in Linear Discriminant Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor pattern classification

IEEE Transactions on Information Theory
An optimization criterion for generalized discriminant analysis on undersampled problems

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust coding schemes for indexing and retrieval from large face databases

IEEE Transactions on Image Processing
Face recognition using the nearest feature line method

IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms

IEEE Transactions on Neural Networks

Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling

Neurocomputing
K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis

Information Sciences: an International Journal
Generalization of linear discriminant analysis using Lp-norm

Pattern Recognition Letters
Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction

Pattern Recognition
Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Pattern Recognition
Linear reconstruction measure steered nearest neighbor classification framework

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

The current discriminant analysis method design is generally independent of classifiers, thus the connection between discriminant analysis methods and classifiers is loose. This paper provides a way to design discriminant analysis methods that are bound with classifiers. We begin with a local mean based nearest neighbor (LM-NN) classifier and use its decision rule to supervise the design of a discriminator. Therefore, the derived discriminator, called local mean based nearest neighbor discriminant analysis (LM-NNDA), matches the LM-NN classifier optimally in theory. In contrast to that LM-NNDA is a NN classifier induced discriminant analysis method, we further show that the classical Fisher linear discriminant analysis (FLDA) is a minimum distance classifier (i.e. nearest Class-mean classifier) induced discriminant analysis method. The proposed LM-NNDA method is evaluated using the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, the ETH80 object category database and the FERET face image database. The experimental results demonstrate the performance advantage of LM-NNDA over other feature extraction methods with respect to the LM-NN (or NN) classifier.