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Summarizing Complexity in High Dimensional Spaces

Karl Young - University of California, San Francisco, USA

As the need to analyze high dimensional, multi-spectral data on complex physical systems becomes more common, the value of methods that glean useful summary information from the data increases. This paper describes a method that uses information theoretic based complexity estimation measures to provide diagnostic summary information from medical images. Implementation of the method would have been difficult if not impossible for a non expert programmer without access to the powerful array processing capabilities provided by SciPy.


K Young, Summarizing Complexity in High Dimensional Spaces in Proceedings of the 7th Python in Science conference (SciPy 2008), G Varoquaux, T Vaught, J Millman (Eds.), pp. 66-69

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