神经影像学
人工智能
计算机科学
非线性降维
机器学习
集成学习
痴呆
疾病
模式识别(心理学)
阿尔茨海默病神经影像学倡议
相似性(几何)
深度学习
歧管(流体力学)
神经科学
心理学
医学
图像(数学)
病理
机械工程
工程类
降维
作者
Baiying Lei,Peng Yang,Yinan Zhuo,Feng Zhou,Dong Ni,Siping Chen,Xiaohua Xiao,Tianfu Wang
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2019-07-01
卷期号:23 (4): 1661-1673
被引量:15
标识
DOI:10.1109/jbhi.2018.2872581
摘要
Alzheimer's disease (AD) is a neurodegenerative and non-curable disease, with serious cognitive impairment, such as dementia. Clinically, it is critical to study the disease with multi-source data in order to capture a global picture of it. In this respect, an adaptive ensemble manifold learning (AEML) algorithm is proposed to retrieve multi-source neuroimaging data. Specifically, an objective function based on manifold learning is formulated to impose geometrical constraints by similarity learning. The complementary characteristics of various sources of brain disease data for disorder discovery are investigated by tuning weights from ensemble learning. In addition, a generalized norm is explicitly explored for adaptive sparseness degree control. The proposed AEML algorithm is evaluated by the public AD neuroimaging initiative database. Results obtained from the extensive experiments demonstrate that our algorithm outperforms the traditional methods.
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