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

arXiv: https://arxiv.org/abs/2401.12842 · HTML: https://mlsanity.com/paper/2401.12842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [A review of learning vector quantization classifiers](https://mlsanity.com/paper/1509.07093/) (2015)
- [Visualisation and knowledge discovery from interpretable models](https://mlsanity.com/paper/2005.03632/) (2020)

## Abstract (arXiv)

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 class-discriminative subspace can be used for dimensionality reduction and the training of robust classifiers with potentially improved performance.