Name
Title | ||
|---|---|---|
Local-Aggregate Modeling for Multi-subject Neuroimage Data via Distributed Optimization |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/PRNI.2013.60 | PRNI |
Keywords | Field | DocType |
algorithmic strategy,optimisation,spatio-temporal nature,generalized linear models,local-aggregate models,multisubject neuroimage data,learning (artificial intelligence),multi-subject neuroimage data,regression analysis,electroencephalography,whole-brain neuroimage data,medical signal processing,multi-subject data,local-aggregate modeling,distributed optimization,fitting multi-subject model,novel multivariate machine learning model,optimization strategy,multi-subject regression,parallel computing,multi-subject eeg example,eeg,multi subject eeg example,multi-subject predictive model,neuroimaging,aggregates information,optimization,learning artificial intelligence,computational modeling,predictive models,data models | Data mining,Regression,Regression analysis,Computer science,Multivariate statistics,Generalized linear model,Regularization (mathematics),Artificial intelligence,Univariate,Machine learning | Conference |
ISSN | Citations | PageRank |
2330-9989 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Hu | 1 | 0 | 0.34 |
| Genevera I. Allen | 2 | 89 | 11.18 |