Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Discriminative Vanishing Component Analysis
Authors: Chenping Hou, Feiping Nie, Dacheng Tao
AAAI 2016 | Venue PDF | 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. |