An improvement to matrix-based LDA

Authors:
Chongyang Zhang;Jingyu Yang
Affiliations:
Nanjing University of Science and Technology, Nanjing, China;Nanjing University of Science and Technology, Nanjing, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

Citing 18
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm

Neural Networks
Face recognition from one example view

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA

Pattern Recognition Letters
An Optimal Set of Discriminant Vectors

IEEE Transactions on Computers
Eigenfaces for recognition

Journal of Cognitive Neuroscience
An approach for directly extracting features from matrix data and its application in face recognition

Neurocomputing
Down-Sampling Face Images and Low-Resolution Face Recognition

ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Advanced Pattern Recognition Technologies with Applications to Biometrics

Advanced Pattern Recognition Technologies with Applications to Biometrics
Improving the interest operator for face recognition

Expert Systems with Applications: An International Journal
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition

Computer Vision and Image Understanding
One improvement to two-dimensional locality preserving projection method for use with face recognition

Neurocomputing
LPP solution schemes for use with face recognition

Pattern Recognition
Input space versus feature space in kernel-based methods

IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The matrix-based LDA method is attracting increasing attention. Compared with classic LDA, this method can overcome the small sample size (SSS) problem. However, previous literatures neglect the fact that there are two available matrix-based LDA algorithms and usually use only one of the two algorithms to perform the experiment. By experimental analysis, this work point out the combination of the two available matrix-based LDA algorithms can obtain a better performance.