| Autor: |
Shuzhi Su, Penglian Gao, Yanmin Zhu, Xingzhu Liang, Jun Xie |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 9, Pp 142653-142663 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3118023 |
| Popis: |
In multi-view learning, massive literature is devoted to exploring the intrinsic structure between cross-views. It is well known that canonical correlation analysis (CCA) is a conventional multi-view learning method, which considers the correlation between two views. However, it fails to utilize class information and is difficult to suit different issues to extract discriminative features. In this paper, we propose a novel cross-view discriminative feature learning method called dynamic discriminative canonical correlation analysis, which captures class information to yield discriminative features. More specifically, we develop an adaptive weight scheme of cross-view within-class and between-class scatters to make full use of distribution class information. In addition, an iterative algorithm with Cauchy inequalities and the Lagrange multiplier is proposed to handle the non-smooth objective function. Our method is applied to face recognition and multi-linguistic text classification tasks. Extensive experimental results reveal that the adaptive weight scheme plays a beneficial role and our method is an effective feature learning. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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