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.
翻译:已经为数据压缩提出了各种高压分解方法。 在高压分解的实际应用中,为给定数据选择高压形状是一个挑战,而高压形状的形状可能会影响错误和压缩比率。在这项工作中,我们研究高压形状对高压分解的影响,并提议一个优化模型,以找到高压列车(TT)分解的最佳形状。拟议的优化模型将TT分解的压缩比率最大化,但有误。我们采用与TT-SVD算法相联系的遗传算法(GA)来解决优化模型。我们采用拟议的RGB图像压缩方法。结果显示了拟议的进化变压变形形状搜索对TT分解的功效。