Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA)
Michael L. Biehl, Sofie Lövdal · 2023
We introduce and investigate the iterated application of Generalized Matrix Relevance Learning for the analysis of feature relevances in classification problems.The suggested Iterated Relevance Matrix Analysis (IRMA), identifies a linear subspace representing the classification specific information of the considered data sets in feature space using Generalized Matrix Learning Vector Quantization.By iteratively determining a new discriminative direction while projecting out all previously identified ones, all features carrying relevant information about the classification can be found, facilitating a detailed analysis of feature relevances.Moreover, IRMA can be used to generate improved low-dimensional representations and visualizations of labeled data sets.