학술논문
Estimating Local Intrinsic Dimension with k-Nearest Neighbor Graphs
Document Type
Conference
Author
Source
IEEE/SP 13th Workshop on Statistical Signal Processing, 2005 Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on. :417-422 2005
Subject
Language
ISSN
2373-0803
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