Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration

Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration

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 fo...

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Bibliographic Details
Main Authors: Keisuke Maeda, Yoshiki Ito, Takahiro Ogawa, Miki Haseyama
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Feature integration
multi-view
canonical correlation analysis
fractional-order technique
geometrical structure
Online Access:https://ieeexplore.ieee.org/document/9121214/

Internet

https://ieeexplore.ieee.org/document/9121214/

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