Notice of RetractionParallel Computing Model of Multiple Dimensions Data Streams Canonical Correlation Analysis with GPU

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

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With view to satisfying the requirement of real-time under the circumstance of resource-constraints, specific and practical architecture for high-dimensional data streams are proposed, meanwhile, based on CUDA (Compute Unified Device Architecture), canonical correlation analysis between two multiple dimensions data streams using data cube pattern and dimensionality-reduction technique is carried out in this framework. The theoretical analysis and experimental results show that the parallel processing method can online detect correlations between multiple dimension data streams accurately in the synchronous sliding window mode. According to the pure CPU method, this method has significant speed advantage, well meeting the real-time requirements of high-dimensional data streaming and can be applied to the field of data stream mining widely.