Two-dimensional canonical correlation analysis and its application in small sample size face recognition

2009 ◽  
Vol 19 (3) ◽  
pp. 377-382 ◽  
Author(s):  
Ning Sun ◽  
Zhen-hai Ji ◽  
Cai-rong Zou ◽  
Li Zhao
Keyword(s):  
Face Recognition ◽  
Correlation Analysis ◽  
Sample Size ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Small Sample Size ◽  
Small Sample ◽  
Two Dimensional

scholarly journals Underwater Acoustic Target Feature Fusion Method Based on Multi-Kernel Sparsity Preserve Multi-Set Canonical Correlation Analysis

2019 ◽  
Vol 37 (1) ◽  
pp. 87-92
Author(s):  
Honghui Yang ◽  
Shuzhen Yi
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Target Recognition ◽  
Small Sample Size ◽  
Small Sample ◽  
Projection Algorithm ◽  
Fusion Method ◽  
Underwater Target

To solve high-dimensional and small-sample-size classification problem for underwater target recognition, a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multi-domain features, remove redundant and noise features, in order to achieve multi-domain feature fusion. The multi-kernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multi-domain feature samples, which enhances the feature's classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.

Kernel Null Space Marginal Fisher Analysis for Face Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1065-1068
Author(s):  
Yu’e Lin ◽  
Xing Zhu Liang ◽  
Hua Ping Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Sample Size ◽  
High Performance ◽  
Null Space ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Original Data ◽  
Small Sample ◽  
Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

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