嵌入空间的低秩张量填充算法

Low Rank Tensor Completion Algorithm in Embedded Space

以高精度低秩张量填充算法为基础,提出了在嵌入空间中的低秩张量填充算法.先将缺失数据经过多向延迟嵌入变为高阶Hankel张量,再基于核范数最小化进行填充,最后使用多向延迟嵌入的逆操作得到最终的填充数据.通过数值实验表明,针对张量的条状或块状缺失,新算法比HaLRTC、DR-TR算法更有效.

Based on the high accuracy low rank tensor completion algorithm,a low rank tensor completion algorithm in embedded space is proposed.Firstly,the missing data is transformed into a higher-order Hankel tensor by multi-way delay embedding.Then,a method is used for completing based on the nulear norm minimization.Finally,the inverse transform is used to obtain the completed data.And experimental results show that the new algorithm is more effective than HaLRTC and DR-TR algorithm for row and column fiber missing of tensor.