Discriminative Vanishing Component Analysis
Authors: Chenping Hou, Feiping Nie, Dacheng Tao
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are provided for demonstration. We would like to provide two groups of experimental results for illustration. |
| Researcher Affiliation | Academia | College of Science, National University of Defense Technology; Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University; Center for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information Technology, University of Technology, Sydney |
| Pseudocode | Yes | Algorithm 1 Discriminative Vanishing Component Analysis (DVCA) |
| Open Source Code | No | The paper mentions that 'VCA is implemented by the code provided by the authors1. 1http://www.cs.huji.ac.il/ rlivni73/', which refers to the code for the baseline VCA, not the authors' own DVCA method. |
| Open Datasets | Yes | All the data are downloaded from open sources2,3. 2http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ 3http://www.cad.zju.edu.cn/home/dengcai/Data/data.html |
| Dataset Splits | Yes | The original data are randomly split into two parts, training and testing samples. The common parameter d is tuned using 5-fold cross validation. |
| Hardware Specification | Yes | We have tested the algorithm by a naive Matlab implementation on a workstation with 12 processor (3.33G for each) and 47.2GB memory. |
| Software Dependencies | No | The paper mentions 'Matlab implementation' and 'Lib Svm software (Chang and Lin 2011)' but does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | No | The paper states that 'The common parameter d is tuned using 5-fold cross validation. Besides, in DVCA, we also determine λ by cross validation.', but does not provide specific values for hyperparameters or detailed training configurations for the models used. |