Coupled Interdependent Attribute Analysis on Mixed Data
Authors: Can Wang, Chi-Hung Chi, Wei Zhou, Raymond Wong
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Substantial experiments on 12 UCI data sets show that our approach can effectively capture the global couplings of heterogeneous attributes and outperforms the state-of-the-art methods, supported by statistical analysis. |
| Researcher Affiliation | Academia | Can Wang, Chi-Hung Chi, Wei Zhou Digital Productivity Flagship, CSIRO, Australia {can.wang, chihung.chi, wei.zhou}@csiro.au Raymond Wong University of New South Wales, Australia wong@cse.unsw.edu.au |
| Pseudocode | No | The paper describes algorithms and methods in prose and equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open or publicly available. |
| Open Datasets | Yes | Experiments are performed on 12 UCI data sets, shown in Table 5. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, or test splits for the datasets, nor does it mention cross-validation details. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions various algorithms and models (e.g., k-means, PCA, Spectral CAT) but does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | As suggested in (Wang, She, and Cao 2013), maximal power L is assigned to be 3 or 4 , whichever performs better. The number of runs is set to be 100 to obtain average results with their sample standard deviations. The number of clusters is fixed to be the number of real classes in each data. |