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Robust generalized canonical correlation analysis
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DOI：10.1007/s10489-023-04666-6.png)
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摘要
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Generalized canonical correlation analysis (GCCA) has been widely used for classification and regression problems. The key idea of GCCA is to map the data from different views into a common space with the minimum reconstruction error. However, GCCA employs the squared Frobenius norm as a distance metric to find a latent correlated space without a specific strategy to cope with outliers, thus misguiding the GCCA's training task in real-world applications and leading to suboptimal performance. This inspires us to propose a novel robust formulation for GCCA, namely, GCCA with the p-order (0
Keyword:
Outliers and noise
p-order of Frobenius norm
Robust RGCCA
Squared Frobenius norm