Abstract
This paper proposes a novel multi-view discriminant analysis based on Hilbert-Schmidt Independence Criterion (HSIC) and canonical correlation analysis (CCA). We use HSIC to identify a lower dimensional discriminant common subspace in which the dependence between multi-view features and the associated labels is maximized. CCA is utilized to achieve maximum correlation between different views in the common subspace. Motivated by the successful application of uncorrelated discriminant analysis, we further extend our approach to extract features with minimum redundancy. Experimental results validate the effectiveness of our proposed approaches.
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Acknowledgements
This work was supported by the Natural Science Foundation of China (Grants NO.61602248), the Natural Science Foundation of Jiangsu Province (Grants No. BK20160741) and the Fundamental Research Funds for the Central Universities (No.KJQN201733).
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Shu, X., Yuan, P., Jiang, H. et al. Multi-view uncorrelated discriminant analysis via dependence maximization. Appl Intell 49, 650–660 (2019). https://doi.org/10.1007/s10489-018-1271-6
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DOI: https://doi.org/10.1007/s10489-018-1271-6