Tensor Shape Search for Optimum Data Compression
This work addresses a domain-specific challenge in tensor decomposition for data compression, but it is incremental as it builds on existing methods like TT-SVD and genetic algorithms.
The paper tackles the problem of selecting tensor shapes for data compression in tensor train decomposition, proposing an optimization model to maximize compression ratio under an error bound and implementing a genetic algorithm with TT-SVD, applied to RGB images to demonstrate effectiveness.
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.