  In this paper, we present a novel computational framework for nonlinear
dimensionality reduction which is specifically suited to process large data
sets: the Exploratory Inspection Machine (XIM). XIM introduces a conceptual
cross-link between hitherto separate domains of machine learning, namely
topographic vector quantization and divergence-based neighbor embedding
approaches. There are three ways to conceptualize XIM, namely (i) as the
inversion of the Exploratory Observation Machine (XOM) and its variants, such
as Neighbor Embedding XOM (NE-XOM), (ii) as a powerful optimization scheme for
divergence-based neighbor embedding cost functions inspired by Stochastic
Neighbor Embedding (SNE) and its variants, such as t-distributed SNE (t-SNE),
and (iii) as an extension of topographic vector quantization methods, such as
the Self-Organizing Map (SOM). By preserving both global and local data
structure, XIM combines the virtues of classical and advanced recent embedding
methods. It permits direct visualization of large data collections without the
need for prior data reduction. Finally, XIM can contribute to many application
domains of data analysis and visualization important throughout the sciences
and engineering, such as pattern matching, constrained incremental learning,
data clustering, and the analysis of non-metric dissimilarity data.
