  We propose a deep structure encoder using the recently introduced 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
contrast to generalized activation functions. Experimental results on two
different datasets have shown a significant improvement in the clustering
performance for VNNs auto-encoder over conventional Convolutional Neural
Networks (CNNs) auto-encoder. In addition, we also show that the proposed
approach demonstrates a much-improved sample complexity over CNN-based
auto-encoder with a superb robust classification performance.
