Previously, we have presented a method for embedding selection based on cluster analysis. In this paper, we described an embedding selection method based on a feature reduction transformation matrix. This method extracts features that are important for maintaining decision boundaries in the supervised clusters. Experimentally, we demonstrate that our method allow accurate prediction of the Mackay-Glass chaotic time series. Three important properties of the feature reduction transformation are proved in this paper.
Feature Reduction, Embedding Selection, Decision Boundary
Date of this Version