The Layer-wise Relevance Propagation assigns to each dimension, or feature, x d a relevance score R < 0 correspond to evidence in favor or against, respectively, the output classification.
more formally, let f : R d → R + be a function that quantifies, for example, the probability of x ∈ R d being in a certain class.