Work

Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis

Year: 2012

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-642-33454-2_33

Authors: Daoqiang Zhang, Jun Liu, Dinggang Shen

Institutions: University of North Carolina at Chapel Hill, Nanjing University of Aeronautics and Astronautics, Siemens (Germany), Siemens (United States)

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Natural Science Foundation of China, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering

Awards: G1600120695, G1610907132, G1761222865, 大规模稀疏学习及其应用研究, Neonatal Brain Segmentation, G2686953907, G3139536118, G3258782064, G4798393751, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, G5273691721, Fast, Robust Analysis of Large Population Data, G6748413423, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G93323634

OpenAlex ID: W205297045

FWCI: 6.469

Cites: 12

Cited by: 23

Related to: 10