We propose a novel filter for sparse big data,called an integrated autoencoder(IAE),which utilises auxiliary information to mitigate data sparsity.The proposed model achieves an appropriate balance between prediction ...We propose a novel filter for sparse big data,called an integrated autoencoder(IAE),which utilises auxiliary information to mitigate data sparsity.The proposed model achieves an appropriate balance between prediction accuracy,convergence speed,and complexity.We implement experiments on a GPS trajectory dataset,and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.展开更多
基金The work was supported by the National Social Science Foundation of China[No.16FJY008]the National Planning Office of Philosophy and Social Science[No.11801060]the Natural Science Foundation of Shandong Province[No.ZR2016FM26].
文摘We propose a novel filter for sparse big data,called an integrated autoencoder(IAE),which utilises auxiliary information to mitigate data sparsity.The proposed model achieves an appropriate balance between prediction accuracy,convergence speed,and complexity.We implement experiments on a GPS trajectory dataset,and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.
