  The principal component analysis network (PCANet) is an unsupervised
parsimonious deep network, utilizing principal components as filters in its
convolution layers. Albeit powerful, the PCANet consists of basic operations
such as principal components and spatial pooling, which suffers from two
fundamental problems. First, the principal components obtain information by
transforming it to column vectors (which we call the amalgamated view), which
incurs the loss of the spatial information in the data. Second, the generalized
spatial pooling utilized in the PCANet induces feature redundancy and also
fails to accommodate spatial statistics of natural images. In this research, we
first propose a tensor-factorization based deep network called the Tensor
Factorization Network (TFNet). The TFNet extracts features from the spatial
structure of the data (which we call the minutiae view). We then show that the
information obtained by the PCANet and the TFNet are distinctive and
non-trivial but individually insufficient. This phenomenon necessitates the
development of proposed HybridNet, which integrates the information discovery
with the two views of the data. To enhance the discriminability of hybrid
features, we propose Attn-HybridNet, which alleviates the feature redundancy by
performing attention-based feature fusion. The significance of our proposed
Attn-HybridNet is demonstrated on multiple real-world datasets where the
features obtained with Attn-HybridNet achieves better classification
performance over other popular baseline methods, demonstrating the
effectiveness of the proposed technique.
