We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a
latent representation of multi-modal data whose features are jointly captured by a union of
subspaces. The so-called self-representation embedding of the latent codes leads to a
simplified fusion which is driven by a similarly constructed decoding. The Volterra Filter
architecture achieved reduction in parameter complexity is primarily due to controlled non-
linearities being introduced by the higher-order convolutions in lieu of generalized activation …