论文标题
张量形状搜索最佳数据压缩
Tensor Shape Search for Optimum Data Compression
论文作者
论文摘要
已经提出了各种张量分解方法以进行数据压缩。在张量分解的现实世界应用中，选择给定数据的张量形状会带来挑战，张量的形状可能会影响误差和压缩比。在这项工作中，我们研究了张量形状对张量分解的影响，并提出了优化模型，以找到张量列（TT）分解的最佳形状。提出的优化模型在给定误差绑定的情况下最大化TT分解的压缩比。我们实施了与TT-SVD算法相关的遗传算法（GA）来求解优化模型。我们将提出的方法应用于RGB图像的压缩。结果证明了提出的进化张量搜索TT分解的有效性。
Various tensor decomposition methods have been proposed for data compression. In real world applications of the tensor decomposition, selecting the tensor shape for the given data poses a challenge and the shape of the tensor may affect the error and the compression ratio. In this work, we study the effect of the tensor shape on the tensor decomposition and propose an optimization model to find an optimum shape for the tensor train (TT) decomposition. The proposed optimization model maximizes the compression ratio of the TT decomposition given an error bound. We implement a genetic algorithm (GA) linked with the TT-SVD algorithm to solve the optimization model. We apply the proposed method for the compression of RGB images. The results demonstrate the effectiveness of the proposed evolutionary tensor shape search for the TT decomposition.
