"Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction

Some approaches [14], [15] aim to infer global feature importance for a given data Transparency of machine learning (ML) based system, projection. Another work [16] estimates feature importance applied in the real world, is nowadays a widely accepted locally for a vicinity around a projected data point, using requirement - the importance of transparency was also recognized locally linear models. A recent paper [17] proposes to use by the policy makers and therefore made its way local feature importance explanations by computing a local into legal regulations like the EU's GDPR [1]. A popular linear approximation for each reduced dimension, extracting way of achieving transparency is by means of explanations [2] feature importances from the weight vectors. Further, saliency which then gave rise to the field of eXplainable AI (XAI) [3], map approaches such as the layer-wise relevance propagation [4]. Although a lot of different explanation methodologies (LRP) [18] could in principle be applied to a parametric for ML based systems have been developed [2], [4], it is dimensionality reduction mapping in order to obtain locally important to realize that it is still somewhat unclear what relevant features. However, these approaches do not provide exactly makes up a good explanation [5], [6]. Therefore contrasting explanations, in which we are interested here.