A Probabilistic Derivation of LASSO and L12-Norm Feature Selections
Authors: Di Ming, Chris Ding, Feiping Nie4586-4593
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | experimental results on six benchmark datasets, including images and bio-microarray data, show that our proposed flexible feature selection method has an overwhelmed advantage over state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Di Ming,a Chris Ding,a Feiping Nieb a Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA b Centre for OPTical Imagery Analysis and Learning, Northwestern Polytechnical University, Xian 710072, China |
| Pseudocode | Yes | Algorithm 1 Efficient algorithm for solving the ℓ1,2-norm based feature selection. 1: Input: Data matrix X Rd n, labels Y Rn k. 2: Output: W Rd k, Dj Rd d, j = 1, , k. 3: Set t = 0. 4: Initialize Wt. 5: repeat 6: for each class j {1, , k} do 7: Compute Dj via Eq.20. 8: Compute wt+1 j via Eq.24. 9: end for 10: Set t = t + 1. 11: until Converges |
| Open Source Code | No | No explicit statement or link regarding the open-sourcing of the code for the methodology described in this paper. |
| Open Datasets | Yes | MNIST2 (Lecun et al. 1998), Bin Alpha3, AT&T4. Each instance is represented by a vector with all the pixel values... Carcinomas (Su et al. 2001), (Yang et al. 2006), Lung (Bhattacharjee et al. 2001), TOX5 (Kwon et al. 2012). ... 3https://cs.nyu.edu/ roweis/data.html 4http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase. html 5http://featureselection.asu.edu/datasets.php |
| Dataset Splits | Yes | Classifiers: k-nearest neighbor (KNN), support vector machine (SVM), and linear regression (LR) with five-fold cross validation are used to evaluate the performance of feature selection on classification. The average of classification performance on different five folds are reported as the final accuracy. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are provided for the experimental setup. |
| Software Dependencies | No | LIBSVM (Chang and Lin 2011) is used as practical implementation of SVM, in which the kernel is set as linear and C = 1. |
| Experiment Setup | Yes | The parameter k in KNN is set as 3. LIBSVM (Chang and Lin 2011) is used as practical implementation of SVM, in which the kernel is set as linear and C = 1. |