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 [1].
Structure Regularized Unsupervised Discriminant Feature Analysis
Authors: Mingyu Fan, Xiaojun Chang, Dacheng Tao
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Clustering and classification experiments on real world image data sets demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | Mingyu Fan College of Maths & Info. Science, Wenzhou University, Wenzhou 325035, China EMAIL Xiaojun Chang Centre for Artificial Intelligence University of Technology Sydney, Sydney, NSW 2007, Australia EMAIL Dacheng Tao Centre for Artificial Intelligence University of Technology Sydney, Sydney, NSW 2007, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1 ADMM for solving problem (8) and Algorithm 2 The algorithm for solving problem (13) |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The experiments are conducted on publicly available image data sets: the Coil-20 data set (Coil-20)1, the USPS handwritten digits data set (USPS)2, the Yale-B Extended (Yale B) and the CMUPIE face data sets3. 1http://www.cs.columbia.edu/CAVE/software/softlib/coil20.php 2http://www.escience.cn/people/fpnie/ 3http://www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html |
| Dataset Splits | Yes | Given a data set, we randomly select p percents from every class in data X to formulate the training set Xtrain, and the left data are used as the test data Xtest. The results when p = 30 is presented here and the results when p = 10 is provided in the supplement material. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For methods require neighborhood sizes for data structure learning, the neighborhood size is searched in {4, 6, 8, 10}. For UDFS, RUFS, and JELSR, the regularization parameters are searched in the range {10 5, 10 4, , 101, 102}. The parameter γA is searched in the range {0.01, 0.05, 0.1, 0.5, 1}. To make the experimental results reproducible, λZ for Sr-UDFS is set as 0.1 respectively throughout the experiments. |