Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces

We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. The suggested Iterated Relevance Matrix Analysis (IRMA) identifies a linear subspace representing the classification specific information of the considered data sets using Generalized Matrix Learning Vector Quantization (GMLVQ). By iteratively determining a new discriminative subspace while projecting out all previously identified ones, a combined subspace carrying all class-specific information can be found. This facilitates a detailed analysis of feature relevances, and enables improved low-dimensional representations and visualizations of labeled data sets. Additionally, the IRMA-based classdiscriminative subspace can be used for dimensionality reduction and the training of robust classifiers with potentially improved performance. Preprint submitted to Neurocomputing January 24, 2024 1. Introduction Prototype-based systems such as Learning Vector Quantization (LVQ) [1, 2, 3, 4] can serve as genuinely interpretable and transparent classification tools [5].