Visualization transforms of non-spatial data for convolutional neural networks

Many datasets in important fields like healthcare and finance are often in a tabular format, where each observation is expressed as a vector of various feature values. While there exist several competitive algorithms such as random forests and gradient boosting, convolutional neural networks (CNNs) are making tremendous strides in terms of new research and applications. In order to exploit the power of convolution neural networks for these tabular datasets, we propose two vector-to-image transformations. One is a direct transformation, while the other is an indirect mechanism to first modulate the latent space of a trained generative adversarial network (GAN) with the observation vectors and then generate the images using the generator. On both simulated and real datasets, we show that CNNs trained on images based on our proposed transforms lead to better predictive performance compared to random forests and neural networks that are trained on the raw tabular datasets.