基于分布式优化的多主体神经图像数据局部聚合建模

Yue Hu, Genevera I. Allen
{"title":"基于分布式优化的多主体神经图像数据局部聚合建模","authors":"Yue Hu, Genevera I. Allen","doi":"10.1109/PRNI.2013.60","DOIUrl":null,"url":null,"abstract":"Developing multi-subject predictive models based on whole-brain neuroimage data for each subject is a major challenge due to the spatio-temporal nature of the variables and the massive amount of data relative to the number of subjects. We propose a novel multivariate machine learning model and algorithmic strategy for multi-subject regression or classification that uses regularization to directly account for the spatio-temporal nature of the data. Our method begins by fitting multi-subject models to each location separately (similar to univariate frameworks), and then aggregates information across nearby locations through regularization. We develop an optimization strategy so that our so called, Local-Aggregate Models, can be fit in a completely distributed manner over the locations which greatly reduces computational costs. Our models achieve better predictions with more interpretable results as demonstrated through a multi-subject EEG example.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Local-Aggregate Modeling for Multi-subject Neuroimage Data via Distributed Optimization\",\"authors\":\"Yue Hu, Genevera I. Allen\",\"doi\":\"10.1109/PRNI.2013.60\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Developing multi-subject predictive models based on whole-brain neuroimage data for each subject is a major challenge due to the spatio-temporal nature of the variables and the massive amount of data relative to the number of subjects. We propose a novel multivariate machine learning model and algorithmic strategy for multi-subject regression or classification that uses regularization to directly account for the spatio-temporal nature of the data. Our method begins by fitting multi-subject models to each location separately (similar to univariate frameworks), and then aggregates information across nearby locations through regularization. We develop an optimization strategy so that our so called, Local-Aggregate Models, can be fit in a completely distributed manner over the locations which greatly reduces computational costs. Our models achieve better predictions with more interpretable results as demonstrated through a multi-subject EEG example.\",\"PeriodicalId\":144007,\"journal\":{\"name\":\"2013 International Workshop on Pattern Recognition in Neuroimaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Workshop on Pattern Recognition in Neuroimaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PRNI.2013.60\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Workshop on Pattern Recognition in Neuroimaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRNI.2013.60","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

由于变量的时空性质和相对于受试者数量的大量数据,基于每个受试者的全脑神经图像数据开发多受试者预测模型是一项重大挑战。我们提出了一种新的多元机器学习模型和算法策略,用于多主题回归或分类,它使用正则化来直接解释数据的时空性质。我们的方法首先将多主题模型分别拟合到每个位置(类似于单变量框架),然后通过正则化聚合附近位置的信息。我们开发了一种优化策略,使我们所谓的局部聚合模型能够以完全分布式的方式适合于各个位置,从而大大降低了计算成本。我们的模型通过一个多主体脑电图例子证明了我们的模型具有更好的预测效果和更多的可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local-Aggregate Modeling for Multi-subject Neuroimage Data via Distributed Optimization
Developing multi-subject predictive models based on whole-brain neuroimage data for each subject is a major challenge due to the spatio-temporal nature of the variables and the massive amount of data relative to the number of subjects. We propose a novel multivariate machine learning model and algorithmic strategy for multi-subject regression or classification that uses regularization to directly account for the spatio-temporal nature of the data. Our method begins by fitting multi-subject models to each location separately (similar to univariate frameworks), and then aggregates information across nearby locations through regularization. We develop an optimization strategy so that our so called, Local-Aggregate Models, can be fit in a completely distributed manner over the locations which greatly reduces computational costs. Our models achieve better predictions with more interpretable results as demonstrated through a multi-subject EEG example.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信