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Investigating relationships between variables in multi-dimensional data setsis a common task for data analysts and engineers. More specifically, it isoften valuable to understand which ranges of which input variables lead toparticular values of a given target variable. Unfortunately, with an increasingnumber of independent variables, this process may become cumbersome andtime-consuming due to the many possible combinations that have to be explored.In this paper, we propose a novel approach to visualize correlations betweeninput variables and a target output variable that scales to hundreds ofvariables. We developed a visual model based on neural networks that can beexplored in a guided way to help analysts find and understand suchcorrelations. First, we train a neural network to predict the target from theinput variables. Then, we visualize the inner workings of the resulting modelto help understand relations within the data set. We further introduce a newregularization term for the backpropagation algorithm that encourages theneural network to learn representations that are easier to interpret visually.We apply our method to artificial and real-world data sets to show its utility.

Visual Neural Decomposition to Explain Multivariate Data Sets