Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction, which mainly includes three parts: tensor dictionary representation, dictionary preprocessing, and dictionary update. Specifically, the tensor is respectively performed by the sparse representation and Tucker decomposition, from which one can obtain the dictionary, sparse coefficient, and core tensor. Furthermore, the sparse representation can be obtained through the relationship between sparse coefficient and core tensor. In addition, the dimensionality of the input tensor is reduced by using the concentrated dictionary learning. Finally, some experiments show that, compared with other algorithms, the proposed algorithm has obvious advantages in preserving the original data information and denoising ability.
Maximum Discriminant Difference Criterion for Dimensionality Reduction of Tensor Data
