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- W3204806758 abstract "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." @default.
- W3204806758 created "2021-10-11" @default.
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- W3204806758 date "2021-01-01" @default.
- W3204806758 modified "2023-09-26" @default.
- W3204806758 title "A Dynamic Discriminative Canonical Correlation Analysis via Adaptive Weight Scheme" @default.
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- W3204806758 doi "https://doi.org/10.1109/access.2021.3118023" @default.
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