A Method for Automatically Finding Interpretations of Reduced Dimension Representations
Methods such as FastMap and Multidimensional Scaling often are used to project data to a lower dimensional subspace to make the data easier to understand. One drawback of these methods is that although it is easier to see patterns in the reduced dimension representation, interpreting the new dimensions is diÆcult. We present an automatic method for nding mappings (associations) between reduced dimension representations and auxiliary features that describe the data. Our approach nds groups of dimensions that taken together preserve local structure in the auxiliary feature space. Unlike previous approaches to this problem, this method works well in the non-linear mappings that often arise with reduced dimension projections. We use the method to assign meaning to dimensions resulting from applying MDS to protein helix pairs.