Abstract:

Aiming at the shortcomings of existing successive missing complement methods, a successive missing data completion method for multi-view depth fusion is established. The method adopts inverse distance weighted interpolation, bidirectional simple exponential smoothing, user-based collaborative filtering, the collaborative filtering based on mass diffusion and structure embeddings, to obtain intermediate results of five missing data in spatiotemporal and semantic respectively; then, this method constructs a neural network model that combines complementary heterogeneous information across time and space and semantic views to achieve successive missing completion. Experimental results show that the method is universally applicable to the field of Spatial-Temporal successive missing sequence completion and, that it not only achieves a high efficiency, but also reduces the mean absolute error and the mean relative error by 7% and 22%, respectively, compared with the Spatial-Temporal Multi-view completion method.

Key words: successive missing completion, artificial neural network, spatial and temporal, deep integration