A Data Reduction Algorithm Based on K-L Feature Compression for Cloud Computing

A K-L (Karhunen-Loeve Transform) based algorithm for reducing the dimension of redundant data in the cloud computing is proposed. High dimensional feature extraction is performed in the reconstructed phase space of redundant data, and K-L feature compression method is adopted to reduce the dimension of redundant data in the cloud computing, and the improved FIR filtering algorithm is designed to realize the redundancy data filtering. Simulation results show that the proposed algorithm can effectively achieve the feature space dimension reduction of the redundant data in the cloud computing, and can improve the speed of cloud computing, and reduce the computational cost.