Feature Clustering for Support Identification in Extreme Regions
Authors: Hamid Jalalzai, Rémi Leluc
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments provide strong empirical evidence of the relevance of our approach. and We perform some numerical experiments in Section 5 to highlight the performance of our method and we finally conclude in Section 6. and We focus on popular machine learning tasks of feature clustering and anomaly detection to compare the performance of our algorithm against state-of-the-art methods for extreme events. (Section 5, Numerical Experiments). |
| Researcher Affiliation | Academia | 1Telecom Paris, Institut Polytechnique de Paris, France 2INRIA, Institut Polytechnique de Paris, France. Correspondence to: Hamid Jalalzai <hamid.jalalzai@inria.fr>, R emi Leluc <remi.leluc@telecom-paris.fr>. |
| Pseudocode | Yes | A detailed pseudo-code of MEXICO is provided below in Algorithm 1. |
| Open Source Code | Yes | For ease of reproducibility, the code is available in the supplementary material. |
| Open Datasets | Yes | Five reference AD datasets are studied: shuttle, forestcover, http, SF and SA. Table 5 in the Appendix provides further dataset details. The algorithms are trained and tested on the same datasets... We consider simulated data from an (asymmetric) logistic distribution where the dependence structure of extremes can be specified (see Appendix B.2). and references KDDCup (1999) and Lichman (2013). UCI machine learning repository. |
| Dataset Splits | No | The parameter setting is the following: dimension p {75, 100, 150, 200}, number of train samples ntrain = 1000 and test samples ntest = 100. The paper does not specify percentages or sample counts for a distinct validation set. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or cloud resources used for running the experiments. |
| Software Dependencies | No | We use the metrics implemented by Scikit-Learn (Pedregosa et al., 2011). No specific version numbers for Scikit-Learn or other software dependencies are provided. |
| Experiment Setup | No | More details about the preprocessing, model tuning and additional results are available in the supplementary material. (Section 5.2). However, specific hyperparameters or training settings are not provided in the main text. |