  We propose a unifying setting that combines existing restricted kernel
machine methods into a single primal-dual multi-view framework for kernel
principal component analysis in both supervised and unsupervised settings. We
derive the primal and dual representations of the framework and relate
different training and inference algorithms from a theoretical perspective. We
show how to achieve full equivalence in primal and dual formulations by
rescaling primal variables. Finally, we experimentally validate the equivalence
and provide insight into the relationships between different methods on a
number of time series data sets by recursively forecasting unseen test data and
visualizing the learned features.
