Stable Feature Selection from Brain sMRI
Authors: Bo Xin, Lingjing Hu, Yizhou Wang, Wen Gao
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the proposed nonnegative model performs much better in exploring the intrinsic structure of data via selecting stable features compared with other state-of-the-arts. |
| Researcher Affiliation | Academia | National Engineering Laboratory for Video Technology, Key Laboratory of Machine Perception, School of EECS, Peking University, Beijing, 100871, China Yanjing Medical College, Capital Medical University, Beijing, 101300, China |
| Pseudocode | No | The paper describes algorithms and mathematical formulations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the described methodology was found. |
| Open Datasets | Yes | The data are obtained from the Alzheimer s Disease Neuroimaging Initiative (ADNI) database2. We split all the baseline data into 1.5T and 3.0T MRI scans datasets (named 15T and 30T). 2http://adni.loni.ucla.edu |
| Dataset Splits | Yes | 10-fold cross-validation (CV) evaluation is applied and the classification accuracy for all tasks are summarized in Tab. 2. |
| Hardware Specification | Yes | All experiments are carried out on an Intel(R) Core(TM) i7-3770 CPU at 3.40GHz. |
| Software Dependencies | Yes | Although off-the-shelf convex solvers such as CVX (Grant and Boyd 2013) can be applied to solve the optimization, it hardly scales to high-dimensional problems in feasible time. |
| Experiment Setup | No | For each model, we used grid-search to find the optimal parameters respectively. No specific hyperparameters, ranges for grid search, or resulting optimal parameters are provided for reproducibility. |