{"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}
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.