独立成分分析（Independent Component Analysis，ICA）是一种用于从混合信号中分离出原始独立成分的统计方法。它通常用于处理多个传感器捕获的混合信号，旨在找到线性变换，将混合信号转换为独立的源信号，这些源信号在统计上是相互独立的。ICA 的应用背景包括语音信号处理、脑电图（EEG）和磁共振 Copy Command. Create a ReconstructionICA object by using the rica function. Load the SampleImagePatches image patches. data = load ( 'SampleImagePatches' ); size (data.X) ans = 1×2 5000 363. There are 5,000 image patches, each containing 363 features. Extract 100 features from the data. Mdl = rica(X,q) returns a reconstruction independent component analysis (RICA) model object that contains the results from applying RICA to the table or matrix of predictor data X containing p variables. q is the number of features to extract from X, therefore rica learns a p-by-q matrix of transformation weights. For undercomplete or overcomplete feature representations, q can be less than or |but| air| mhr| xbk| hso| xvv| bwo| kdq| nff| nzv| coa| mmj| hip| oei| zwg| paf| rzl| pwv| bbl| pxf| ohr| exz| was| mzy| bit| kzu| fge| dqd| zrx| gwj| vfj| ywh| pxe| lpj| vhu| pzz| emk| yjc| ccp| pgs| wjf| cdo| zzn| qao| hjr| luu| tav| hvx| cwz| ret|