Structure Regularized Unsupervised Discriminant Feature Analysis
Authors: Mingyu Fan, Xiaojun Chang, Dacheng Tao
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 fanmingyu@wzu.edu.cn Xiaojun Chang Centre for Artificial Intelligence University of Technology Sydney, Sydney, NSW 2007, Australia cxj273@gmail.com Dacheng Tao Centre for Artificial Intelligence University of Technology Sydney, Sydney, NSW 2007, Australia dacheng.tao@uts.edu.au |
| 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. |