Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Authors: Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 provides experimental validations and applications, including an improved version of LDA/QDA based on the proposed enhanced estimates. sections 4.1. Validation on synthetic data and 4.2. Application to LDA/QDA contain figures and tables demonstrating empirical results. |
| Researcher Affiliation | Academia | 1Centrale Sup elec, University Paris Saclay, France 2GIPSAlab, University Grenoble-Alpes, France 3LISTIC, University Savoie Mont-Blanc, France. |
| Pseudocode | Yes | Algorithm 1 Proposed estimation algorithm. and Algorithm 2 Improved estimation from linear shrinkage. |
| Open Source Code | Yes | Matlab codes for the proposed estimation algorithms are available at https://github. com/maliktiomoko/RMTCov Est and are based on Manopt, a Matlab toolbox for optimization on manifolds (Boumal et al., 2014). |
| Open Datasets | Yes | The bottom right displays are applications to EEG data from (Andrzejak et al., 2001). |
| Dataset Splits | No | The paper mentions 'training vectors' for LDA/QDA but does not explicitly provide specific train/validation/test dataset splits or cross-validation details for reproduction. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | Yes | Matlab codes for the proposed estimation algorithms are available at https://github. com/maliktiomoko/RMTCov Est and are based on Manopt, a Matlab toolbox for optimization on manifolds (Boumal et al., 2014). |
| Experiment Setup | No | The paper describes the gradient descent algorithm, step size 't' (fixed or optimized by backtracking line search), and initializations (M0 = Ip or linear shrinkage), but it does not provide specific numerical values for hyperparameters like fixed step size or details for the backtracking line search. |