Random matrix theory improved Fréchet mean of symmetric positive definite matrices
Authors: Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frederic Pascal
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation, involving both synthetic and real-world EEG and hyperspectral datasets, shows that we largely outperform stateof-the-art methods. |
| Researcher Affiliation | Collaboration | 1Universit e Paris Saclay, CNRS, Centrale Sup elec, L2S 2Universit e Savoie Mont Blanc, LISTIC 3Huawei Paris Research Center. |
| Pseudocode | Yes | Algorithm 1 Covariance based on RMT corrected distance, Algorithm 2 RMT corrected Fr echet mean on S++ p, Algorithm 3 Nearest Centroid classifier based on RMT, Algorithm 4 K-Means clustering based on RMT |
| Open Source Code | Yes | To ensure reproducibility, the code for the experiments discussed is accessible at https://github.com/ Ammar Mian/icml-rmt-2024. |
| Open Datasets | Yes | motor imagery datasets accessible via the MOABB platform (Aristimunha et al., 2023). |
| Dataset Splits | No | The paper mentions a 'training set' in the context of the Nearest Centroid algorithm, but it does not provide specific train/validation/test split percentages, sample counts, or explicit instructions for how the datasets were partitioned for the empirical evaluations. |
| Hardware Specification | No | The development and evaluation of these methods were conducted in Python. Specifically, SCM and LW implementations were sourced from the scikit-learn library (Pedregosa et al., 2018), while LW-NL comes from scikit-RMT3. The conventional Fr echet means, standard Nearest Centroid and K-Means algorithms were taken from the py Riemann library (Barachant et al., 2023). |
| Software Dependencies | No | The development and evaluation of these methods were conducted in Python. Specifically, SCM and LW implementations were sourced from the scikit-learn library (Pedregosa et al., 2018), while LW-NL comes from scikit-RMT3. The conventional Fr echet means, standard Nearest Centroid and K-Means algorithms were taken from the py Riemann library (Barachant et al., 2023). |
| Experiment Setup | Yes | Parameters are p = 64, K = 10 on the left and n = 128 on the right. (from Figure 1 caption under Simulations) and 'The K-means algorithm, capped at 100 iterations with early stopping at a 10^-4 tolerance'. |