Iterated Relevance Matrix Analysis (IRMA) for the identification of
  class-discriminative subspaces
Sofie Lövdal Michael Biehl
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.