Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks

We propose convolutional deep averaging networks (CDANs) for classifying and learning feature representations of datasets containing instances with unordered features, where each feature is considered a tuple composed of one or more values. CDANs accept variable-size input and are invariant to permutations of the input's order. In addition, as a side-effect of the training process, CDANs learn discriminative, nonlinear embeddings of individual input elements into a space of chosen dimensionality. Contrary to their name, which is inspired by the work of Iyyer et al. [11], CDANs could perhaps be more accurately termed convolutional deep pooling networks as we also consider the effects of functions other than averaging such as taking element-wise maximums or sums. A. Contributions We propose CDANs for classifying unordered feature sets. We show that a CDAN with nonlinear embeddings is competitive with and perhaps even superior to recurrent neural networks (RNNs) and known permutation-invariant architectures for classifying instances containing variablesize sets of unordered features. We also find that the type of pooling plays a significant role in determining the efficacy of the network with sum-pooling clearly outperforming maxand average-pooling.