Existing algorithms for canonical correlation analysis(CCA) of multidimensional data streams are mostly based on approximate techniques

but are not the precise algorithms for updates in essence.In this study

a novel canonical correlation analysis algorithm

called TCCA(Tracking CCA)

is proposed for tracking the correlations rapidly and accurately between two multidimensional data streams in the time-varying environments.By introducing the technique of rank two modifications to update the eigen-subspace of the sample covariance matrix in parallel

TCCA can rapidly track the correlations of data streams.Theoretical analysis and experimental results indicate that the TCCA algorithm has better stability

high computational efficiency and accuracy.It could be presented as a basic tool for correlation detection on data streams

feature fusion

dimension reduction and other areas of data streams mining.