Label-wise Orthogonal Canonical Correlation Analysis and Its Application to Image Recognition

被引：1
|
作者：
Ping, Xinrui [1 ]
Zhao, Qianjin [1 ]
Su, Shuzhi [1 ,2 ]
机构：
[1] Anhui Univ Sci & Technol, Sch Math & Big Data, Huainan, Peoples R China
[2] Hefei Comprehens Natl Sci Ctr, Inst Energy, Hefei, Peoples R China
来源：
PROCEEDINGS OF 2021 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INFORMATION SYSTEMS (ICAIIS '21) | 2021年
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Feature fusion; discriminative learning; orthogonal projection; image recognition; ASSOCIATION;
D O I：
10.1145/3469213.3470288
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes the label-wise orthogonal canonical correlation analysis (LOCCA), which constrains the label-based relationships and orthogonalizes correlation projection directions. In the method, the discriminative structures constrained by class labels are effectively preserved, and the correlation projection directions from LOCCA reduce the information redundancy by orthogonality criterion as much as possible. Encouraging experimental results on two real-world image datasets reflect that our method is an effective and robust image recognition method.
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页数：5
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