Feature Learning using Multi-view Kernel Partial Least Squares

The multi-view learning deals with data of multiple views, aiming to explore the underlying relations between different views and use them for various tasks. In this paper, we derive a multi-view extension of kernel partial least squares for unsupervised feature learning. We establish the optimization objective in the primal as the pairwise covariance between the projection scores and derive that this model can be trained in the dual form by solving an eigenvalue problem. Experiments are also conducted to verify the effectiveness of the method with real-life multi-view datasets, where the proposed method is adopted as a feature extractor and then the clustering task is conducted for performance comparisons.