Robust Unsupervised Learning via L-statistic Minimization
Authors: Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments with KMEANS clustering and principal subspace analysis demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1Istituto Italiano di Tecnologia, Genoa, Italy 2University of Genoa, Genoa, Italy 3University of Milan, Milan, Italy 4University College London, London, UK. |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We used the Fashion-MNIST dataset which consists of about 70000 28 28 images of various types of clothes splitted in a training set of 60000 images and a test set of 10000 images. |
| Dataset Splits | No | The paper mentions a training set of 60000 images and a test set of 10000 images, but it does not specify a separate validation set or its size. |
| Hardware Specification | Yes | All experiments have been run on an standard laptop equipped with an Intel i9 with 8 cores each working at 2,4 GHz and 16 GB of RAM DDR4 working at 2,6 GHz. |
| Software Dependencies | No | The paper states, 'For KMEANS++ we used the sklearn implementation', but it does not provide any specific version numbers for sklearn or any other software dependencies. |
| Experiment Setup | Yes | For both RKM and KMEANS++ we T = 10 and r = 30. We initialized RKM with uniform centers and set ζ = 0.75, the same ζ is used for SD. ... We run the algorithms with T = 50, r = 30, M = 4000, k = 2 and ζ in the range [0.4, 1]. |