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• W2151427378 abstract "Manifold learning algorithms rely on a neighbourhood graph to provide an estimate of the data’s local topology. Unfortunately, current methods for estimating local topology assume local Euclidean geometry and locally uniform data density, which often leads to poor data embeddings. We address these shortcomings by proposing a framework that combines local learning with parametric density estimation for local topology estimation. Given a data set D X , we rst estimate a new metric space ( X;dX) that characterizes the varying sample density ofX in X, then use (X;dX) as a new (pilot) input space for the graph construction step of the manifold learning process. The proposed framework results in signicantly improved embeddings, which we demonstrated objectively by assessing clustering accuracy." @default.
• W2151427378 created "2016-06-24" @default.
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• W2151427378 creator A5025690493 @default.
• W2151427378 creator A5059908049 @default.
• W2151427378 date "2013-10-21" @default.
• W2151427378 modified "2023-09-24" @default.
• W2151427378 title "Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning" @default.
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• W2151427378 hasPublicationYear "2013" @default.
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