In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion methods.These methods aim to...In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion methods.These methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders.However,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data.This is where data-driven multi-modal fusion techniques come into play.By combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed.In this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component analysis.The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain disorders.After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical translation.Overall,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.展开更多
实际工业过程中往往包含不同运行工况,且每种工况数据一般不服从同一种分布.数据的多分布性和分布的不确定性使得传统的故障诊断方法难以获得满意的效果,因此提出一种基于局部邻域和贝叶斯推断的多工况故障诊断方法.首先,通过局部邻域...实际工业过程中往往包含不同运行工况,且每种工况数据一般不服从同一种分布.数据的多分布性和分布的不确定性使得传统的故障诊断方法难以获得满意的效果,因此提出一种基于局部邻域和贝叶斯推断的多工况故障诊断方法.首先,通过局部邻域标准化算法对多工况数据进行预处理;再利用ICA-PCA(independent component analysis and principal component analysis)方法分别对该数据集的高斯特性和非高斯特性进行分析处理,获得全局模型;然后结合贝叶斯推断将多个统计量组合成一个监测统计量,实现多工况过程的在线监测;最后通过数值例子和TE过程的仿真研究,验证了提出方法的可行性和有效性.展开更多
基金supported by the Natural Science Foundation of China (62373062,82022035)the China Postdoctoral Science Foundation (2022M710434)+1 种基金the National Institute of Health grants (R01EB005846,R01MH117107,and R01MH118695)the National Science Foundation (2112455).
文摘In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion methods.These methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders.However,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data.This is where data-driven multi-modal fusion techniques come into play.By combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed.In this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component analysis.The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain disorders.After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical translation.Overall,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
文摘实际工业过程中往往包含不同运行工况,且每种工况数据一般不服从同一种分布.数据的多分布性和分布的不确定性使得传统的故障诊断方法难以获得满意的效果,因此提出一种基于局部邻域和贝叶斯推断的多工况故障诊断方法.首先,通过局部邻域标准化算法对多工况数据进行预处理;再利用ICA-PCA(independent component analysis and principal component analysis)方法分别对该数据集的高斯特性和非高斯特性进行分析处理,获得全局模型;然后结合贝叶斯推断将多个统计量组合成一个监测统计量,实现多工况过程的在线监测;最后通过数值例子和TE过程的仿真研究,验证了提出方法的可行性和有效性.
