Stable, robust and super fast reconstruction of tensors using multi-way projections

Cesar F. Caiafa and Andrej Cichocki.

Serie: Trabajos publicados del IAR ; no. 1291

Resumen: In the framework of multidimensional Compressed Sensing (CS), we introduce an analytical reconstruction formula that allows one to recover an N th-order data tensor \underline {\bf X}in{BBR[elevado a la]{I_{1}\times I_{2}\times\cdots \times I_{N}}} from a reduced set of multi-way compressive measurements by exploiting its low multilinear-rank structure. Moreover, we show that, an interesting property of multi-way measurements allows us to build the reconstruction based on compressive linear measurements taken only in two selected modes, independently of the tensor order N . In addition, it is proved that, in the matrix case and in a particular case with 3rd-order tensors where the same 2D sensor operator is applied to all mode-3 slices, the proposed reconstruction \underline {\bf X}_{\tau } is stable in the sense that the approximation error is comparable to the one provided by the best low-multilinear-rank approximation, where \tau is a threshold parameter that controls the approximation error. Through the analysis of the upper bound of the approximation error we show that, in the 2D case, an optimal value for the threshold parameter \tau =\tau _{0}>0 exists, which is confirmed by our simulation results. On the other hand, our experiments on 3D datasets show that very good reconstructions are obtained using \tau =0, which means that this parameter does not need to be tuned. Our extensive simulation results demonstrate the stability and robustness of the method when it is applied to real-world 2D and 3D signals. A c- mparison with state-of-the-arts sparsity based CS methods specialized for multidimensional signals is also included. A very attractive characteristic of the proposed method is that it provides a direct computation, i.e., it is non-iterative in contrast to all existing sparsity based CS algorithms, thus providing super fast computations, even for large datasets.