Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model
Authors: Jie Xu, Cheng Deng, Xinbo Gao, Dinggang Shen, Heng Huang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on cognitive data of the ADNI cohort demonstrate the superior performance of the proposed method. |
| Researcher Affiliation | Academia | Jie Xu1,3, Cheng Deng1, Xinbo Gao1, Dinggang Shen2, Heng Huang3,1 1Xidian University, Xi an 710071, China 2Department of Radiology and BRIC, UNC-Chapel Hill, USA 3University of Texas at Arlington, USA |
| Pseudocode | Yes | Algorithm 1 The algorithm to solve Eq. (6) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | we evaluate prediction performance of the proposed method by applying it to Alzheimer s Disease Neuroimaging Initiative (ADNI) cohort (adni.loni.usc.edu) |
| Dataset Splits | Yes | In all experiments, we automatically tune the regularization parameters by selecting among the values {10r : r { 5, ..., 5}} with standard 5-fold cross-validation strategy. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we automatically tune the regularization parameters by selecting among the values {10r : r { 5, ..., 5}} with standard 5-fold cross-validation strategy. |