作业帮有哪位高手能帮我翻译此英语论文!
来源：学生作业帮 编辑：作业帮 分类：英语作业 时间：2024/04/29 23:47:04
有哪位高手能帮我翻译此英语论文!
Preface
Independent componnent analysis(ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables,measurements,or signals.ICA defines a generative model for observed multivariables date,which is typically given as a large database of samples.In the model,the data variables are assumed to be linear or nonlinear mixture of some unknown latent variables,and the mixing system is also unknown.The latent variables are assumed nongaussian and mutually independent,and they are called the independent components of the observed data.These independent components,also called sources or factors by ICA.
ICA can be seen as an extension to principal component analysis and factor analysis.ICA is a much more powerful technique,however,capable of finding the underlying factors or sources when these classic methods fail completely.
The data analyzed by ICA could originate from many different kinds of application fields,including digital images and document databases,as well as economic indacators psychometric measurements.In many cases,the measurements are given as a set of parallel singnals or time series;the term blind source separation is used to chractrize this problem.Typical examples are mixtures of simultaneous speech signals that have been picked up by several microphones,brain waves recorded by mutiple sensors,interfering radio signal arriving at a mobile phone,or parallel time series obtained from some industrial process.
Preface
Independent componnent analysis(ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables,measurements,or signals.ICA defines a generative model for observed multivariables date,which is typically given as a large database of samples.In the model,the data variables are assumed to be linear or nonlinear mixture of some unknown latent variables,and the mixing system is also unknown.The latent variables are assumed nongaussian and mutually independent,and they are called the independent components of the observed data.These independent components,also called sources or factors by ICA.
ICA can be seen as an extension to principal component analysis and factor analysis.ICA is a much more powerful technique,however,capable of finding the underlying factors or sources when these classic methods fail completely.
The data analyzed by ICA could originate from many different kinds of application fields,including digital images and document databases,as well as economic indacators psychometric measurements.In many cases,the measurements are given as a set of parallel singnals or time series;the term blind source separation is used to chractrize this problem.Typical examples are mixtures of simultaneous speech signals that have been picked up by several microphones,brain waves recorded by mutiple sensors,interfering radio signal arriving at a mobile phone,or parallel time series obtained from some industrial process.
前言
独立componnent分析（ ICA ）是一种统计和计算技术,揭示隐藏的因素,背后套随机变量,测量,或signals.ICA生成模式定义了一个多变量的观测日期,通常是作为一个大型数据库的samples.In该模型的数据变量假定为线性或非线性混合一些未知的隐变量,以及混合系统也unknown.The隐变量假定nongaussian和相互独立的,他们被称为独立的组成部分的观测数据.这些独立的组成部分,也称为来源或因素的控区.
国际合作社联盟可以看作是一个延伸主成分分析和因子analysis.ICA是一个更强大的技术,但是,能够找到的根本因素或消息时,这些传统的方法完全失败.
数据分析了国际合作社联盟可能来自许多不同种类的应用领域,包括数字图像和文件的数据库,以及经济indacators心理measurements.In许多情况下,测量得到的一组平行singnals或时间序列;任期盲源分离是用来chractrize这个例子是混合物problem.Typical同时语音信号已拾起几个麦克风,脑电波记录的多传感器,干扰无线电信号到达移动电话,或平行时间序列从一些工业过程.
独立componnent分析（ ICA ）是一种统计和计算技术,揭示隐藏的因素,背后套随机变量,测量,或signals.ICA生成模式定义了一个多变量的观测日期,通常是作为一个大型数据库的samples.In该模型的数据变量假定为线性或非线性混合一些未知的隐变量,以及混合系统也unknown.The隐变量假定nongaussian和相互独立的,他们被称为独立的组成部分的观测数据.这些独立的组成部分,也称为来源或因素的控区.
国际合作社联盟可以看作是一个延伸主成分分析和因子analysis.ICA是一个更强大的技术,但是,能够找到的根本因素或消息时,这些传统的方法完全失败.
数据分析了国际合作社联盟可能来自许多不同种类的应用领域,包括数字图像和文件的数据库,以及经济indacators心理measurements.In许多情况下,测量得到的一组平行singnals或时间序列;任期盲源分离是用来chractrize这个例子是混合物problem.Typical同时语音信号已拾起几个麦克风,脑电波记录的多传感器,干扰无线电信号到达移动电话,或平行时间序列从一些工业过程.