This page has been superseded by a new version on GitHub, please visit https://github.com/radacha/tle.
Welcome to the support page for the Tight Local intrinsic dimensionality Estimator (TLE) described in the paper:
Laurent Amsaleg, Oussama Chelly, Michael E. Houle, Ken-ichi
Kawarabayashi, Miloš Radovanović and Weeris Treeratanajaru. Intrinsic
dimensionality estimation within tight localities. In Proceedings of the SIAM
International Conference on Data Mining (SDM), pages 181–189,
Calgary, Alberta, Canada, 2019 [pdf]
and the accompanying supplement [pdf].
Matlab code implementing intrinsic dimensionality (ID) estimators and experiments described in the paper is available here.
Real data sets are available here (~490MB).
Synthetic data sets from the "m-family" are available here (~120MB).
The released code is licensed under the Creative Commons
Attribution-ShareAlike 4.0 International License:
For attribution, please cite the above paper.