Finding the most interpretable MDS rotation for sparse linear models based on external features.

Abstract

One approach to interpreting multidimensional scaling (MDS) embeddings is to estimate a linear relationship between the MDS dimensions and a set of external features. However, because MDS only preserves distances between instances, the MDS embedding is invariant to rotation. As a result, the weights characterizing this linear relationship are arbitrary and difficult to interpret. This paper proposes a procedure for selecting the most pertinent rotation for interpreting a 2D MDS embedding.

Publication
ESANN
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