Graph Enhanced Canonical Correlation Analysis and Its Application to Image Recognition

Shuzhi SU; Jun XIE; Xinrui PING; Penglian GAO

doi:10.11999/JEIT210154

Volume 43 Issue 11

Nov. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(11): 3342-3349

Shuzhi SU, Jun XIE, Xinrui PING, Penglian GAO. Graph Enhanced Canonical Correlation Analysis and Its Application to Image Recognition[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3342-3349. doi: 10.11999/JEIT210154

Citation:

Shuzhi SU, Jun XIE, Xinrui PING, Penglian GAO. Graph Enhanced Canonical Correlation Analysis and Its Application to Image Recognition[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3342-3349. doi: 10.11999/JEIT210154

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Graph Enhanced Canonical Correlation Analysis and Its Application to Image Recognition

doi: 10.11999/JEIT210154

Shuzhi SU^{1, 2
,
,},
Jun XIE¹,
Xinrui PING³,
Penglian GAO¹

1.
School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
2.
Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230031, China
3.
School of Mathematics and Big Data, Anhui University of Science & Technology, Huainan 232001, China

Funds: The National Natural Science Foundation of China (61806006), The China Postdoctoral Science Foundation (2019M660149), The Institute of Energy, Hefei Comprehensive National Science Center (19KZS203)

Received Date: 2021-02-18
Rev Recd Date: 2021-05-20

Available Online: 2021-06-04

Publish Date: 2021-11-23

Abstract

Abstract

As a traditional feature extraction algorithm, Canonical Correlation Analysis (CCA) has been excellently used for the field of pattern recognition. It aims to find the projection direction that makes the maximum of the correlation between two groups of modal data. However, since the algorithm is an unsupervised linear method, it can not use intrinsic geometry structures and supervised information hidden in data, which will cause difficulty in dealing with high-dimensional nonlinear data. Therefore, this paper proposes a new nonlinear feature extraction algorithm, namely Graph Enhanced Canonical Correlation Analysis (GECCA). The algorithm uses different components of the data to construct multiple component graphs, which retains effectively the complex manifold structures between the data. The algorithm utilizes the probability evaluation method to use class label information, and the graph enhancement method is utilized to integrate the geometry manifolds and the supervised information into the typical correlation analysis framework. Targeted experiments are designed on the face and handwritten digital image datasets to evaluate the algorithm. Good experimental results show the advantages of GECCA in image recognition.
- Pattern recognition,
- Canonical Correlation Analysis (CCA),
- Component graph,
- Probability evaluation

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References(19)

References

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