A comprehensive model combining tensor calculation and few-shot learning is proposed to address the problem of limited and difficult-to-obtain samples in intelligent transportation scenarios such that the issue of unsatisfactory training effect caused by insufficient samples in the target domain can be solved.A multi-dimensional computing model is constructed based on tensor calculation,multi-dimensional heterogeneous data in intelligent transportation scenarios are processed,fused data tensors are obtained based on the spatio-temporal correlation of the data,fused data are used as input data,training is performed using few-shot learning models,and the performances of tensor few-shot learning models based on different tensor calculation schemes and ablation experimental results are compared and analyzed.Simulation results show that compared with two metric-based few-shot learning models,i.e.,the prototype network and matching network,the combination of a meta-learning-based few-shot learning model and a tensor calculation model presents higher credibility.Moreover,by adopting different tensor-fusion schemes,the accuracy and Fl values of the meta-learning model improved to varying degrees.The model based on the inverse-decomposition tensor-fusion scheme offers a maximum accuracy of 0.95,which renders it superior to the CANDECOMP/PARAFAC Decomposition(CPD)fusion scheme in terms of performance.
维普
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