Discriminant analysis approach using fuzzy fourfold subspaces model

Authors:
Xiaoning Song;Xibei Yang;Jingyu Yang;Xiaojun Wu;Yujie Zheng
Affiliations:
Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China and Department of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 2 ...;Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China and Department of Computer Science, San Jose State University, San Jose, CA 95192, USA;Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China;School of Information Engineering, Jiangnan University, Wuxi 214122, PR China;The 28th Research Institute of China Electronics Technology Group Cooperation, Nanjing 210007, PR China
Venue:
Neurocomputing
Year:
2010

Citing 21
Cited 2

The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Regularized discriminant analysis for the small sample size problem in face recognition

Pattern Recognition Letters
KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace Analysis Using Random Mixture Models

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

Pattern Recognition Letters
Separating Style and Content with Bilinear Models

Neural Computation
Improved-LDA based face recognition using both facial global and local information

Pattern Recognition Letters
Solving the small sample size problem in face recognition using generalized discriminant analysis

Pattern Recognition
A linear discriminant analysis framework based on random subspace for face recognition

Pattern Recognition
Neighborhood discriminant projection for face recognition

Pattern Recognition Letters
Linear dimensionality reduction by maximizing the Chernoff distance in the transformed space

Pattern Recognition
Feature extraction for one-class classification problems: Enhancements to biased discriminant analysis

Pattern Recognition
A note on two-dimensional linear discriminant analysis

Pattern Recognition Letters
Boosting random subspace method

Neural Networks
Boosting k-nearest neighbor classifier by means of input space projection

Expert Systems with Applications: An International Journal
Face recognition using a fuzzy fisherface classifier

Pattern Recognition
Generalizing discriminant analysis using the generalized singular value decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence
A shape- and texture-based enhanced Fisher classifier for face recognition

IEEE Transactions on Image Processing
Multilinear Discriminant Analysis for Face Recognition

IEEE Transactions on Image Processing
Integrating Discriminant and Descriptive Information for Dimension Reduction and Classification

IEEE Transactions on Circuits and Systems for Video Technology

Weighted generalized kernel discriminant analysis using fuzzy memberships

WSEAS Transactions on Mathematics
A fuzzy supervised learning method with dynamical parameter estimation for nonlinear discriminant analysis

Computers & Mathematics with Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, some studies have been made on the essence of a novel fuzzy discriminant analysis (FDA) on the fourfold-objective model (FOM). First, a fourfold-objective model on the discriminant analysis is developed, by which a set of integrated subspaces derived from within-class and between-class scatter matrices are constructed, respectively. Second, an improved FDA (IFDA) algorithm based on the relaxed normalized condition is proposed to achieve the distribution information of each sample represented with fuzzy membership grade, which is incorporated into the redefinition of Fisher's scatter matrices. Therefore, the presented algorithm has the potential to outperform the traditional subspace learning algorithms, especially in the cases of small sample size. Experimental results conducted on the ORL, NUST603, FERET and Yale face image databases demonstrate the effectiveness of the proposed method.