Metric Learning in Dimensionality Reduction
Alexander Schulz, Barbara Hammer · 2015
The emerging big dimensionality in digital domains causes the need of powerful non-linear dimensionality reduction techniques for a rapid and intuitive visual data access. While a couple of powerful non-linear dimensionality reduction tools have been proposed in the last years, their applicability is limited in practice: since a non-linear projection is no longer characterised by semantically meaningful data dimensions, the visual display provides only very limited interpretability which goes beyond mere neighbourhood relationships and, hence, interactive data analysis and further expert insight are hindered. In this contribution, we propose to enhance non-linear dimensionality reduction techniques by a metric learning framework. This allows us to quantify the relevance of single data dimensions and their correlation with respect to the given visual display; on the one side, this explains its most relevant factors; on the other side, it opens the way towards an interactive data analysis by changing the data representation based on the learned metric from the visual display.