Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces
This method addresses the need for better feature relevance analysis and dimensionality reduction in classification tasks, but it appears incremental as it builds on existing Generalized Matrix Learning Vector Quantization techniques.
The authors tackled the problem of identifying class-discriminative subspaces in classification by introducing Iterated Relevance Matrix Analysis (IRMA), which iteratively finds linear subspaces using Generalized Matrix Learning Vector Quantization to improve low-dimensional representations and enable robust classifier training.
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.