Monthly
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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

July 2017, Vol. 29, No. 7, Pages 1838-1878
(doi: 10.1162/NECO_a_00969)
© 2017 Massachusetts Institute of Technology
Local Intrinsic Dimension Estimation by Generalized Linear Modeling
Article PDF (3.48 MB)
Abstract

We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to a regression model, we estimate the goodness of fit. Then, by using the maximum likelihood method, we estimate the local intrinsic dimension around the inspection point. The proposed method is shown to be comparable to conventional methods in global intrinsic dimension estimation experiments. Furthermore, we experimentally show that the proposed method outperforms a conventional local dimension estimation method.