@inproceedings{52700dd1bd714a85aa94217a371701c7,
title = "Non-sparse infinite-kernel learning for automated identification of Alzheimer's disease using PET imaging",
abstract = "Multi-kernel learning machine (MKLM) has recently been introduced to the research of computer-aided dementia identification and pathology progress tracking. Despite its good performance especially in case of using heterogeneous data, such learning schema and its variants usually utilize a L-l norm constraint that promotes sparse solutions, which may cause loss of potentially important information. In this paper, we propose the non-sparse infinite-kernel learning machine (NS-IKLM) for automated identification of Alzheimer cases from normal controls. In our approach, a modified constraint is utilized to promotes non-sparse solutions and kernel parameters are automatically tuned during the learning process. The proposed algorithm has been evaluated on a set of FDG-PET images selected from the Alzheimer's disease neuroimaing initiative (ADNI) cohort. Our results demonstrate that the proposed non-sparse NS-IKLM is able to achieve satisfying dementia identification at a relatively low computational cost.",
keywords = "ADNI, dementia, FDG-PET, Infinite kernel learning",
author = "Yong Xia and Shen Lu and Wei Wei and Feng, {David Dagan} and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 ; Conference date: 10-12-2014 Through 12-12-2014",
year = "2014",
doi = "10.1109/ICARCV.2014.7064416",
language = "英语",
series = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "855--860",
booktitle = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014",
}