Work

Joint Sparse and Low-Rank Regularized Multi-Task Multi-Linear Regression for Prediction of Infant Brain Development with Incomplete Data

Year: 2017

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-319-66182-7_5

Authors: Ehsan Adeli, Yu Meng, Gang Li, Weili Lin, Dinggang Shen

Institution: University of North Carolina at Chapel Hill

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of Mental Health

Awards: Infant Brain Measurement and Super-Resolution Atlas Construction, UNC/UMN Baby Connectome Project, 4D Cortical Surface Analysis Tools for Study of Early Brain Development in Typical and High-risk Infants, Constructing 4-dimensional Infant Cortical Surface Atlases

OpenAlex ID: W2751018451

FWCI: 0.489

Cites: 13

Cited by: 6

Related to: 10