  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.
