Duality in Multi-View Restricted Kernel Machines

While kernel methods have shown excellent performance and generalization capabilities, they tend to fall behind when We propose a unifying setting that combines existing it comes to large-scale problems due to their memory and restricted kernel machine methods into a single computational complexity. Additionally, it can be difficult primal-dual multi-view framework for kernel to change their architecture to allow for hierarchical principal component analysis in both supervised representation learning, which is one of the most powerful and unsupervised settings. We derive the primal capabilities of neural networks. Recently, Restricted and dual representations of the framework and Kernel Machines (RKM), were proposed which connect relate different training and inference algorithms least-squares support vector machines and kernel principal from a theoretical perspective. We show how to component analysis (kernel PCA) with Restricted Boltzmann achieve full equivalence in primal and dual formulations machines (Suykens, 2017). RKMs extend the primal by rescaling primal variables. Finally, and dual model representations present in least-squares support we experimentally validate the equivalence and vector machines, from shallow to deep architectures by provide insight into the relationships between different introducing the dual variables as hidden features through methods on a number of time series data conjugate feature duality. This provides a framework of sets by recursively forecasting unseen test data kernel methods represented by visible and hidden units as and visualizing the learned features.