  This paper introduces a novel framework for generative models based on
Restricted Kernel Machines (RKMs) with joint multi-view generation and
uncorrelated feature learning, called Gen-RKM. To enable joint multi-view
generation, this mechanism uses a shared representation of data from various
views. Furthermore, the model has a primal and dual formulation to incorporate
both kernel-based and (deep convolutional) neural network based models within
the same setting. When using neural networks as explicit feature-maps, a novel
training procedure is proposed, which jointly learns the features and shared
subspace representation. The latent variables are given by the
eigen-decomposition of the kernel matrix, where the mutual orthogonality of
eigenvectors represents the learned uncorrelated features. Experiments
demonstrate the potential of the framework through qualitative and quantitative
evaluation of generated samples on various standard datasets.

Machine Learning;Machine Learning