Discovering Structure in High-Dimensional Data Through Correlation Explanation
Authors: Greg Ver Steeg, Aram Galstyan
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that Correlation Explanation (Cor Ex) automatically discovers meaningful structure for data from diverse sources including personality tests, DNA, and human language. |
| Researcher Affiliation | Academia | Greg Ver Steeg Information Sciences Institute University of Southern California Marina del Rey, CA 90292 gregv@isi.edu Aram Galstyan Information Sciences Institute University of Southern California Marina del Rey, CA 90292 galstyan@isi.edu |
| Pseudocode | Yes | Algorithm 1: Pseudo-code implementing Correlation Explanation (Cor Ex) |
| Open Source Code | Yes | Open source code is available at http://github.com/gregversteeg/Cor Ex. |
| Open Datasets | Yes | Data and full list of questions are available at http://personality-testing.info/ _rawdata/. Data, descriptions of SNPs, and detailed demographics of subjects is available at ftp://ftp.cephb. fr/hgdp_v3/. [14] K. Bache and M. Lichman. UCI machine learning repository, 2013. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits for its experiments. It mentions generating samples but not how they are partitioned for model training and evaluation. |
| Hardware Specification | No | The paper mentions GPUs in the context of future scalability for neural networks but does not specify the hardware used for its own experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn' in a reference but does not specify that it was used in their experiments with a version number, nor does it list other software dependencies with versions. |
| Experiment Setup | No | The paper describes general aspects of the optimization and mentions that parameters like \lambda and \gamma can be set through arguments described in Sec. B, but the actual concrete values for hyperparameters or other system-level training settings are not provided in the main text. |