Feature Fusion Method Based on Label-sensitive Multi-set Orthogonal Correlation

ZHAO Qianjin; PING Xinrui; SU Shuzhi; XIE Jun

doi:10.11999/JEIT210323

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3458-3464

ZHAO Qianjin, PING Xinrui, SU Shuzhi, XIE Jun. Feature Fusion Method Based on Label-sensitive Multi-set Orthogonal Correlation[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3458-3464. doi: 10.11999/JEIT210323

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ZHAO Qianjin, PING Xinrui, SU Shuzhi, XIE Jun. Feature Fusion Method Based on Label-sensitive Multi-set Orthogonal Correlation[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3458-3464. doi: 10.11999/JEIT210323

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Feature Fusion Method Based on Label-sensitive Multi-set Orthogonal Correlation

doi: 10.11999/JEIT210323

1.
School of Mathematics and Big Data, Anhui University of Science & Technology, Huainan 232001, China
2.
College of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China

Funds: The National Natural Science Foundation of China (61806006), The China Postdoctoral Science Foundation (2019M660149)

Received Date: 2021-04-19
Rev Recd Date: 2022-08-25

Available Online: 2022-08-30

Publish Date: 2022-10-19

Abstract

Abstract

As a classic feature fusion method, Canonical Correlation Analysis (CCA) is widely used in the field of pattern recognition. Its goal is to learn the relevant projection direction to maximize the correlation between the two sets of variables, but the class label information of the sample and the information between samples redundancy are not considered, which affects the supervisory sensitivity and discriminative power of the fused features. To this end, a label-sensitive Multi-set Discriminant Orthogonal Canonical Correlation Analysis (MDOCCA) feature fusion method is proposed. This method is based on canonical correlation analysis theory. The class label information is embedded into the feature fusion framework, and the orthogonal constraint is added to ensure the maximum fusion of features. Irrelevant, the redundancy of feature information is reduced and the discrimination is improved. Some experiments on multiple image data sets show that this method is an effective feature fusion method.
- Feature fusion,
- Canonical Correlation Analysis (CCA),
- Multi-set,
- Discriminative,
- Orthogonal

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References(22)

References

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