Compressive Spectral Clustering
Authors: Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the performance of our method on artificial and real-world network data. |
| Researcher Affiliation | Collaboration | INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, FR-35042 Rennes Cedex, France Institute of Electrical Engineering, Ecole Polytechnique F ed erale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland Technicolor, 975 Avenue des Champs Blancs, 35576 Cesson-S evign e, France |
| Pseudocode | Yes | Algorithm 1 Spectral Clustering (Ng et al., 2002) |
| Open Source Code | Yes | All our results are reproducible with the CSCbox downloadable at http:// cscbox.gforge.inria.fr/. |
| Open Datasets | Yes | We first perform well-controlled experiments on the Stochastic Block Model (SBM)... |
| Dataset Splits | No | No explicit details on train/validation/test dataset splits are provided for either the synthetic or real-world datasets. |
| Hardware Specification | Yes | Experiments were done on a laptop with a 2.60 GHz Intel i7 dual-core processor running OS Fedora release 22 with 16 GB of RAM. |
| Software Dependencies | Yes | Implementation was done in Matlab R2015a... The fast filtering part of CSC uses the gsp cheby op function of the GSP toolbox (Perraudin et al., 2014). |
| Experiment Setup | Yes | Input: The Laplacian matrix L, the number of clusters k; and parameters typically set to n = 2k log k, d = 4 log n, p = 50 and γ = 10 3. |