Top-k Supervise Feature Selection via ADMM for Integer Programming
Authors: Mingyu Fan, Xiaojun Chang, Xiaoqin Zhang, Di Wang, Liang Du
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments have been conducted on benchmark data sets to show the effectiveness of proposed method. In this section, the proposed method is compared with stateof-the-art supervised feature selection methods on benchmark image data sets. The experiments include the supervised classification by the Nearest Neighbor classifier (1-NN) and the Support Vector Machine (SVM) under various experimental settings. The numerical convergence analysis of the proposed method is also included. |
| Researcher Affiliation | Academia | Mingyu Fan1, Xiaojun Chang2, Xiaoqin Zhang1 , Di Wang1, Liang Du3 1School of Maths & Info. Science, Wenzhou University, Wenzhou 325035, China 2School of Computer Science, Carnegie Mellon University, PA 15213, USA 3School of Computer & Information Technology, Shanxi University, Taiyuan 030006 China |
| Pseudocode | Yes | Algorithm 1 ADMM for solving problem (7) Input: Data matrix X, label matrix Y , γ; A is initialized as the identity matrix I, v = 1D, v1 = v2 = 0D, ρ = 1, and µ = 1.05 Output: Projection matrix A and vector v 1: while not converged do 2: Update A(t+1) as in (10); 3: Update v(t+1) as in (11); 4: Update v(t+1) 1 and v(t+1) 2 through projections onto Sb and Sp as in (12); 5: Update y(t+1) 1 , y(t+1) 2 , y(t+1) 3 and ρ as Eq. (13). 6: If not converged, set t t + 1. 7: end while |
| Open Source Code | Yes | The Matlab code is published online1. 1https://github.com/cxj273/IJCAI2017_1274 |
| Open Datasets | Yes | The Coil-20 data set2 contains 1440 image samples from 20 classes and each image is transformed into a 1024-dimensional data point. There are 72 samples in each class. 2http://www.cs.columbia.edu/CAVE/software/softlib/coil20.php The MNIST handwritten digital image data set3 has 6996 data points of digits 0 9 . Each sample is a 784 dimensional feature vector. 3http://www.escience.cn/people/fpnie/ There are 2114 frontal-face images of 38 individuals in the Yale-B face image data set4. Each image is stacked to a 1024-dimensional data vector. 4http://www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html |
| Dataset Splits | No | Given a data set, we randomly select p percents from each class to formulate the training data Xtrain and the remaining data are used as the test data. No explicit mention of a separate validation dataset split was found; the paper describes a train/test split. |
| Hardware Specification | No | No specific hardware details (e.g., CPU or GPU models, memory size, or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'The Matlab code is published online' but does not specify the version of Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The Spectral method requires the neighborhood size as a key parameter, which is tuned in the range {4, 6, 8, 10}. The regularization parameter λA for the RFS and DLSR methods is searched in the range {0.001, 0.05, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1}. To make our results reproducible, the regularization parameter γ = 0.2 is used for our method throughout the experiments. ... The percentage of labeled training data is p = 30. ... 50 percents of data in each class are used as the training data (p=50) and the top 200 features are utilized. |