Variable Selection Using Deep Variational Information Bottleneck with Drop-Out-One Loss

The information bottleneck (IB) model aims to find the optimal representations of input variables with respect to the response variable. While it has been widely used in the machine-learning community, research from the perspective of the information-theoretic method has been rarely reported regarding variable selection. In this paper, we investigate DNNs for variable selection through an information-theoretic lens. To be specific, we first state the rationality of variable selection with IB and then propose a new statistic to measure the variable importance. On this basis, a new algorithm based on a deep variational information bottleneck is developed to calculate the statistic, in which we consider the Gaussian distribution and the exponential distribution to estimate the Kullback-Leibler divergence. Empirical evaluations on simulated and real-world data show that the proposed method performs better than classical variable-selection methods. This confirms the feasibility of the variable selection from the perspective of IB.