InteractionNN: A Neural Network for Learning Hidden Features in Sparse
Prediction
Abstract
In this paper, we present a neural network (InteractionNN) for sparse predictive analysis where hidden features of sparse data can be learned by multilevel feature interaction. To characterize multilevel
interaction of features, InteractionNN consists of
three modules, namely, nonlinear interaction pooling, layer-lossing, and embedding. Nonlinear interaction pooling (NI pooling) is a hierarchical structure and, by shortcut connection, constructs lowlevel feature interactions from basic dense features
to elementary features. Layer-lossing is a feedforward neural network where high-level feature
interactions can be learned from low-level feature
interactions via correlation of all layers with target.
Moreover, embedding is to extract basic dense features from sparse features of data which can help in
reducing our proposed model computational complex. Finally, our experiment evaluates on the two
benchmark datasets and the experimental results
show that InteractionNN performs better than most
of state-of-the-art models in sparse regression