Partial least squares feature learning via general covariance matrices

In this paper, we develop a novel general covariance matrices based partial least squares (GCMPLS) method for learning latent features from high-dimensional datasets. GCMPLS utilizes Gaussian kernel mappings to map the features in the input spaces to higher-dimensional spaces, thereby being able to extract the nonlinear relationship between two-view features. It uses a simply weighted fusion strategy to fuse general covariance matrices and adopts an alternating iteration method to solve. The effectiveness of this method is validated through a series of experiments. The results indicate that GCMPLS outperforms traditional methods on multiple datasets.