We present a novel method for structure-preserving dimen- sionality reduction. The Exploration Machine (Exploratory Observation Machine, XOM) computes graphical representations of high-dimensional observations by a strategy of self-organized model adaptation. Although simple and computationally ecient, XOM enjoys a surprising flexibility to simultaneously contribute to several dierent domains of advanced ma- chine learning, scientific data analysis, and visualization, such as structure- preserving dimensionality reduction and data clustering. 1 Motivation The exceedingly growing amount of high-dimensional information stored in com- puter-accessible data bases and web resources raises the question of how to organize and extract useful knowledge from this abundant material. Here, a key issue is structure-preserving data reduction which has moved into the focus of interest as an issue of high-priority research eorts. Structure-preserving data reduction can frequently be accomplished by two alternative approaches, namely data partitioning in the sense of 'clustering' and dimensionality reduction often referred to as 'embedding'. In this paper, we introduce a novel algorithm called 'Exploration Machine' (Exploratory Observation Machine - XOM) that unexpectedly can approach both domains from a unified viewpoint within a single computational framework. Af- ter explaining the XOM algorithm, we present applications to the analysis of mul- tidimensional biomedical data from dierent real-world domains: whole-genome microarray gene expression experiments and functional MRI analysis. These two dierent examples convey XOM's capability to simultaneously contribute to the aforementioned dierent challenges of pattern recognition, namely structure- preserving dimensionality reduction and clustering.
['Axel Wismüller']
The Exploration Machine - A Novel Method for Structure-Preserving Dimensionality Reduction