Adaptive tensor train learning algorithm based on single-aspect streaming model - Baoze MA; Guojun LI; Long XING; Changrong YE (Tongxin xuebao)
An adaptive tensor train (TT) learning algorithm for the online decomposition problem of high-order tensors in single-aspect streaming model was investigated.Firstly, it was deduced that single-aspect streaming increment only changes the dimension of temporal TT core.Secondly, the forgetting factor and regularization item were introduced to construct the objective function of exponentially weighted least-squares.Finally, the block-coordinate descent learning strategy was used to estimate the temporal and non-temporal TT core tensors respectively.Simulation results demonstrate that the proposed algorithm is validated in terms of increment size, TT-rank, noise and time-varying intensity, the average relative error and operation time are smaller than that of the comparison algorithms.The tensor slice reconstruction ability is superior than that of the comparison algorithms in the video adaptive analysis.