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Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration
- Source :
- IEEE Access, Vol 8, Pp 114340-114353 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE (Institute of Electrical and Electronics Engineers), 2020.
-
Abstract
- Techniques for integrating different types of multiple features effectively have been actively studied in recent years. Multiset canonical correlation analysis (MCCA), which maximizes the sum of pairwise correlations of inter-view (i.e., between different features), is one of the powerful methods for integrating different types of multiple features, and various MCCA-based methods have been proposed. This work focuses on a supervised MCCA variant in order to construct a novel effective feature integration framework. In this paper, we newly propose supervised fractional-order embedding geometrical multi-view CCA (SFGMCCA). This method constructs not only the correlation structure but also two types of geometrical structures of intra-view (i.e., within each feature) and inter-view simultaneously, thereby realizing more precise feature integration. This method also supports the integration of small sample and high-dimensional data by using the fractional-order technique. We conducted experiments using four types of image datasets, i.e., MNIST, COIL-20, ETH-80 and CIFAR-10. Furthermore, we also performed an fMRI dataset containing brain signals to verify the robustness. As a result, it was confirmed that accuracy improvements using SFGMCCA were statistically significant at the significance level of 0.05 compared to those using conventional representative MCCA-based methods.
- Subjects :
- Multiset
General Computer Science
Computer science
business.industry
geometrical structure
General Engineering
Pattern recognition
Image (mathematics)
Feature (computer vision)
Robustness (computer science)
multi-view
Embedding
General Materials Science
Feature integration
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
Canonical correlation
business
lcsh:TK1-9971
MNIST database
canonical correlation analysis
fractional-order technique
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....f4a97c5f74dd285897111095202804df