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Learning biomedical data locally using diffusion geometry techniques.
, 125-131 (2012)
In recent years, diffusion geometry techniques have been used in many applications of pattern classification and data visualization of high dimensional unstructured data. Our recent research has laid the mathematical foundations for query modeling on such data. We describe some features of this research, and demonstrate how the ideas can be applied fruitfully to the analysis of Cleveland Heart Disease Database and retinal multi-spectral imaging data. Even though our techniques are general purpose rather than tailored to any one application, our methods outperform such classical techniques as support vector machines in some examples.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Data Visualization ; Diffusion Geometry ; Query Modeling
Quellenangaben Pages: 125-131
Institute(s) Institute of Computational Biology (ICB)