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• W2118020210 abstract "Many high-dimensional data sets of practical interest exhibit a varying complexity in different parts of the data space. This is the case, for example, of databases of images containing many samples of a few textures of different complexity. Such phenomena can he modeled by assuming that the data lies on a collection of manifolds with different intrinsic dimensionalities. In this extended abstract, we introduce a method to estimate the local dimensionality associated with each point in a data set, without any prior information about the manifolds, their quantity and their sampling distributions. The proposed method uses a global dimensionality estimator based on k-nearest neighbor (k-NN) graphs, together with an algorithm for computing neighborhoods in the data with similar topological properties" @default.
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• W2118020210 date "2005-01-01" @default.
• W2118020210 modified "2023-09-26" @default.
• W2118020210 title "Estimating Local Intrinsic Dimension with k-Nearest Neighbor Graphs" @default.
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• W2118020210 doi "https://doi.org/10.1109/ssp.2005.1628631" @default.
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