Authors:
Alexander Schulz
and
Barbara Hammer
Affiliation:
Bielefeld University, Germany
Keyword(s):
Dimensionality Reduction, Metric Learning, Interpretability, Data Visualisation.
Related
Ontology
Subjects/Areas/Topics:
Embedding and Manifold Learning
;
Pattern Recognition
;
Theory and Methods
Abstract:
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 analy
sis by changing the data representation based on the learned metric from the visual display.
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