The exploration machine: a novel method for analyzing high-dimensional data in computer-aided diagnosis

Abstract

Purpose: To develop, test, and evaluate a novel unsupervised machine learning method for computer-aided diagnosis and analysis of multidimensional data, such as biomedical imaging data. Methods: We introduce the Exploration Machine (XOM) as a method for computing low-dimensional representations of high-dimensional observations. XOM systematically inverts functional and structural components of topology-preserving mappings. By this trick, it can contribute to both structure-preserving visualization and data clustering. We applied XOM to the analysis of whole-genome microarray imaging data, comprising 2467 79-dimensional gene expression profiles of Saccharomyces cerevisiae, and to model-free analysis of functional brain MRI data by unsupervised clustering. For both applications, we performed quantitative comparisons to results obtained by established algorithms. Results: Genome data: Absolute (relative) Sammon error values were 5.91·10⁵ (1.00) for XOM, 6.50·10⁵ (1.10) for Sammon's mapping, 6.56·10⁵ (1.11) for PCA, and 7.24·105 (1.22) for Self-Organizing Map (SOM). Computation times were 72, 216, 2, and 881 seconds for XOM, Sammon, PCA, and SOM, respectively. - Functional MRI data: Areas under ROC curves for detection of task-related brain activation were 0.984 ± 0.03 for XOM, 0.983 ± 0.02 for Minimal-Free-Energy VQ, and 0.979 ± 0.02 for SOM. Conclusion: For both multidimensional imaging applications, i.e. gene expression visualization and functional MRI clustering, XOM yields competitive results when compared to established algorithms. Its surprising versatility to simultaneously contribute to dimensionality reduction and data clustering qualifies XOM to serve as a useful novel method for the analysis of multidimensional data, such as biomedical image data in computer-aided diagnosis.