Author(s): Nikolaos Mylonas, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
Appeared In: Expert Systems with Applications
Keywords: Dimensionality reduction, Interpretability, Local, Model-agnostic
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Abstract: Dimensionality reduction is a crucial area in artificial intelligence that en- ables the visualization and analysis of high-dimensional data. The main use of dimensionality reduction is to lower the dimensional complexity of data, improving the performance of machine learning models. Non-linear dimensionality reduction approaches, which provide higher quality represen- tations than linear ones, lack interpretability, prohibiting their application in tasks requiring interpretability. This paper presents LXDR (Local eXpla- nation of Dimensionality Reduction), a local, model-agnostic technique that can be applied to any DR technique. LXDR trains linear models around a neighborhood of a specific instance and provides local interpretations using a variety of neighborhood generation techniques. Variations of the proposed technique are also introduced. The effectiveness of LXDR’s interpretations is evaluated by quantitative and qualitative experiments, as well as demonstra- tions of its practical implementation in diverse use cases. The experiments emphasize the importance of interpretability in dimensionality reduction and how LXDR reinforces it.