INTRINSIC DIMENSIONALITY ESTIMATION
by Matthias Hein and Jean-Yves Audibert
On this website you can download the code and datasets for intrinsic dimensionality estimation as described in the paper. Together with the new estimator two classical estimates, the correlation dimension and the Takens estimator, are computed.
These files are all included in one zip-file. Ensure in the unzipping process that the folder structure is preserved.
|GetDim.cpp||Plain C++ code for dimensionality estimation. There are two options. Either one uses data from a file or one generates data. For a detailed description see the ReadMe file.|
|matlab\GetDim.cpp||Mex-File for the use in MATLAB. Here the argument is simply the data matrix (sparse matrices are allowed). Output are the three estimates of the intrinsic dimension. For a detailed description see the ReadMe file.|
|matlab\GenerateManifoldData.cpp||Mex-File for the generation of manifold data as they were used in the paper (and more). For a detailed description see the ReadMe file.|
|matlab\Dimension_Exp.m||Matlab function in order to repeat the experiments as they have been reported in the paper. In order to do that one needs the datasets: Sinusoid, Sphere, Gauss, Moebius and M12. For a detailed description see the ReadMe file.|
download:code (updated: 23.6.2016 - should now work with current Matlab versions/compilers)
We provide here the data as it was used in the paper so that an exact comparison with another method is possible.
|Sinusoid||90 runs of 400, 500 and 600 points. download:sinusoid|
|Sphere||90 runs of 600, 800, 1000 and 1200 points in 4, 6, 8 and 10 dimensions. download:sphere|
|Gaussian||90 runs of 100, 200, 400 and 800 points in 3, 4, 5 and 6 dimensions.download:gauss|
|Moebius||90 runs of 20, 40, 80 and 120 points.download:moebius|
|M12||90 runs of 200, 400, 800 and 1600 points.download:m12|
In the paper the results for the 12-dimensional submanifold of R^72 for 800 points have been accidently exchanged with the ones for 600 points. The correct results for 800 points are 71 - 74 -77.
M. Hein and J.-Y. Audibert, Intrinsic dimensionality estimation of submanifolds in Euclidean space, Proceedings of the 22nd Internatical Conference on Machine Learning (ICML), 289--296. (Eds.) L. de Raedt and S. Wrobel (2005). download:paper