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- W1489592463 abstract "In this paper we propose using manifolds modeled by probabilistic principle surfaces (PPS) to characterize and classify high-D data. The PPS can be thought of as a nonlinear probabilistic generalization of principal components, as it is designed to pass through the “middle” of the data. In fact, the PPS can map a manifold of any simple topology (as long as it can be described by a set of ordered vector co-ordinates) to data in high-dimensional space. In classification problems, each class of data is represented by a PPS manifold of varying complexity. Experiments using various PPS topologies from a 1-D line to 3-D spherical shell were conducted on two toy classification datasets and three UCI Machine Learning datasets. Classification results comparing the PPS to Gaussian Mixture Models and K-nearest neighbours show the PPS classifier to be promising, especially for high-D data." @default.
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- W1489592463 date "2005-01-01" @default.
- W1489592463 modified "2023-10-16" @default.
- W1489592463 title "Probabilistic Principal Surface Classifier" @default.
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- W1489592463 doi "https://doi.org/10.1007/11540007_163" @default.
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