Robust Flexible Feature Selection via Exclusive L21 Regularization
Authors: Di Ming, Chris Ding
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on twelve benchmark datasets demonstrate the effectiveness of the proposed regularization and the optimization algorithm as compared to state-of-the-arts. |
| Researcher Affiliation | Academia | Di Ming and Chris Ding Department of Computer Science and Engineering, University of Texas at Arlington, USA initialdiming@yahoo.com, chqding@uta.edu |
| Pseudocode | Yes | Algorithm 1 Search the largest coordinate τ of S. and Algorithm 2 ALM based optimization algorithm for solving the exclusive ℓ2,1 regularization in problem (14). |
| Open Source Code | No | No explicit statement about releasing the source code for the described methodology or a link to a code repository was found. |
| Open Datasets | Yes | Experiments on twelve benchmark datasets are conducted to evaluate the performance of feature selection methods on classification. Among those benchmarks, there are 4 image datasets: MNIST3 [Lecun et al., 1998], Yale4, Yale B5 , PIE [Sim et al., 2002]; 1 spoken letter recognition dataset: ISOLET6; 5 bio-microarray datasets: Carcinomas [Yang et al., 2006], Lung [Bhattacharjee et al., 2001], Glioma [Nutt et al., 2003], TOX6, Tumor-14 [Ramaswamy et al., 2001]; and 2 text datasets: CNAE-9 [Ciarelli and Oliveira, 2009], 20Newsgroups7. Footnotes provide links: http://vision.ucsd.edu/content/yale-face-database, http://www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html, http://featureselection.asu.edu/datasets.php, http://qwone.com/ jason/20Newsgroups/ |
| Dataset Splits | Yes | To evaluate the performance on classification, 5-fold crossvalidation accuracy with SVM as classifier are computed on average. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, memory, or cloud instance types used for experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'LIBSVM [Chang and Lin, 2011] is used as the practical implementation of SVM', but it does not provide a specific version number for LIBSVM or any other software dependencies. |
| Experiment Setup | Yes | LIBSVM [Chang and Lin, 2011] is used as the practical implementation of SVM, where kernel is set as linear and parameter C is set as 1 for all the experiments. For the proposed algorithm, parameters are initialized as: t = 0, νt = 1/ X F , ρ = 1.1, ϵ1 = 1e 8, ϵ2 = 1e 5, Λt = 0, random initialization weights Wt. Also, for convergence study, α=1, β =1. |