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FINE: Fisher Information Nonparametric Embedding
2009
IEEE Transactions on Pattern Analysis and Machine Intelligence
We consider the problems of clustering, classification, and visualization of high-dimensional data when no straightforward euclidean representation exists. In this paper, we propose using the properties of information geometry and statistical manifolds in order to define similarities between data sets using the Fisher information distance. We will show that this metric can be approximated using entirely nonparametric methods, as the parameterization and geometry of the manifold is generally
doi:10.1109/tpami.2009.67
pmid:19762935
fatcat:3oyjjiyohncb3psuuamqmexuhq