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Learning Manifolds: Design Analysis for Medical Applications
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Author(s): Diana Mateus (Technische Universität München, Germany & Helmholtz Zentrum München, Germany), Christian Wachinger (Technische Universität München, Germany), Selen Atasoy (Technische Universität München, Germany & Imperial College London, UK), Loren Schwarz (Technische Universität München, Germany)and Nassir Navab (Technische Universität München, Germany)
Copyright: 2012
Pages: 29
Source title:
Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis
Source Author(s)/Editor(s): Kenji Suzuki (University of Chicago, USA)
DOI: 10.4018/978-1-4666-0059-1.ch018
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Abstract
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-points, manifold learning algorithms first approximate the low dimensional manifold where data lives with a graph; then, they find a non-linear map to embed this graph into a low dimensional space. Since the explicit pairwise relations and the neighborhood system can be designed according to the application, manifold learning methods are very flexible and allow easy incorporation of domain knowledge. The authors describe different assumptions and design elements that are crucial to building successful low dimensional data representations with manifold learning for a variety of applications. In particular, they discuss examples for visualization, clustering, classification, registration, and human-motion modeling.
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