Multivariate Maximal Correlation Analysis
Authors: Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that MAC outperforms existing solutions, is robust to noise, and discovers interesting and useful patterns. |
| Researcher Affiliation | Academia | 1 Karlsruhe Institute of Technology, Germany 2 University of Antwerp, Belgium 3 Max-Planck Institute for Informatics & Saarland University, Germany |
| Pseudocode | No | The paper describes the proposed method in Section 5, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | For comparability and repeatability of our experiments we provide data, code, and parameter settings on our project website.2 (Footnote 2: http://www.ipd.kit.edu/~nguyenh/mac) |
| Open Datasets | Yes | We use 7 labeled data sets from different domains (N d): Musk (6598 166), Letter Recognition (20000 16), Pen Digits (7494 16), Waveform (5000 40), WBCD (569 30), Diabetes (768 8), and Glass (214 9), taken from the UCI ML repository. We apply MAC on a real-world data set containing climate and energy consumption measures of an office building in Frankfurt, Germany (Wagner et al., 2014). |
| Dataset Splits | No | The paper mentions generating synthetic data and using existing datasets but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as CPU or GPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions software components like "MIC", "DCOR", "Apriori subspace search framework", "DBSCAN", "ENCLUS", and "CMI" as baselines or methods used, but it does not provide specific version numbers for any of these or for general programming languages/libraries. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes, number of epochs) or other system-level training settings for their proposed MAC method. |