Global optimal discriminant vectors under the global Fisher discriminant criterion

1994 ◽  
Author(s):  
Yong-Ge Wu ◽  
Ke Liu ◽  
Lei-Jian Liu ◽  
Jingyu Yang
Keyword(s):  
Fisher Discriminant ◽  
Fisher Discriminant Criterion ◽  
Global Optimal

Discriminant Criteria for Pattern Classification

2011 ◽  
pp. 30-57
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Feature Extraction ◽  
Pattern Classification ◽  
The Other ◽  
Linear Projection ◽  
Linear Discriminant ◽  
Large Margin ◽  
The Third ◽  
Fisher Discriminant ◽  
Squared Error ◽  
Fisher Discriminant Criterion

As mentioned in Chapter II, there are two kinds of LDA approaches: classification- oriented LDA and feature extraction-oriented LDA. In most chapters of this session of the book, we focus our attention on the feature extraction aspect of LDA for SSS problems. On the other hand,, with this chapter we present our studies on the pattern classification aspect of LDA for SSS problems. In this chapter, we present three novel classification-oriented linear discriminant criteria. The first one is large margin linear projection (LMLP) which makes full use of the characteristic of the SSS problems. The second one is the minimum norm minimum squared-error criterion which is a modification of the minimum squared-error discriminant criterion. The third one is the maximum scatter difference which is a modification of the Fisher discriminant criterion.

UNCORRELATED LOCAL FISHER DISCRIMINANT ANALYSIS FOR FACE RECOGNITION

2011 ◽  
Vol 25 (06) ◽  
pp. 863-887 ◽  
Author(s):  
HONG HUANG ◽  
JIAMIN LIU ◽  
HAILIANG FENG
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Fisher Discriminant Analysis ◽  
Global Optimal Solution ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Global Optimal ◽  
Eigen Decomposition

An improved manifold learning method, called Uncorrelated Local Fisher Discriminant Analysis (ULFDA), for face recognition is proposed. Motivated by the fact that statistically uncorrelated features are desirable for dimension reduction, we propose a new difference-based optimization objective function to seek a feature submanifold such that the within-manifold scatter is minimized, and between-manifold scatter is maximized simultaneously in the embedding space. We impose an appropriate constraint to make the extracted features statistically uncorrelated. The uncorrelated discriminant method has an analytic global optimal solution, and it can be computed based on eigen decomposition. As a result, the proposed algorithm not only derives the optimal and lossless discriminative information, but also guarantees that all extracted features are statistically uncorrelated. Experiments on synthetic data and AT&T, extended YaleB and CMU PIE face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of the proposed method.

Enhanced fisher discriminant criterion for image recognition

2012 ◽  
Vol 45 (10) ◽  
pp. 3717-3724 ◽  
Author(s):  
Quanxue Gao ◽  
Jingjing Liu ◽  
Haijun Zhang ◽  
Jun Hou ◽  
Xiaojing Yang
Keyword(s):  
Image Recognition ◽  
Fisher Discriminant ◽  
Fisher Discriminant Criterion
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