Backtransformation: a new representation of data processing chains with a scalar decision function

Data processing often transforms a complex signal using a set of different preprocessing algorithms to a single value as the outcome of a final decision function. Still, it is challenging to understand and visualize the interplay between the algorithms performing this transformation. Especially when dimensionality reduction is used, the original data structure (e.g., spatio-temporal information) is hidden from subsequent algorithms. To tackle this problem, we introduce the backtransformation concept suggesting to look at the combination of algorithms as one transformation which maps the original input signal to a single value. Therefore, it takes the derivative of the final decision function and transforms it back through the previous processing steps via backward iteration and the chain rule. The resulting derivative of the composed decision function in the sample of interest represents the complete decision process. Using it for visualizations might improve the understanding of the process. Often, it is possible to construct a feasible processing chain with affine mappings which simplifies the calculation for the backtransformation and the interpretation of the result a lot. In this case, the affine backtransformation provides the complete parameterization of the processing chain. This article introduces the theory, provides implementation guidelines, and presents three application examples.