Tensor based geology preserving reservoir parameterization with Higher Order Singular Value Decomposition (HOSVD)

Tensor based geology preserving reservoir parameterization with Higher Order Singular Value Decomposition (HOSVD)

Parameter estimation through robust parameterization techniques has been addressed in many works associated with history matching and inverse problems. Reservoir models are in general complex, nonlinear, and large-scale with respect to the large number of states and unknown parameters. Thus, having a practical approach to replace the original set of highly correlated unknown parameters with non...

Using the Higher Order Singular Value Decomposition (HOSVD) for Video Denoising

We present an algorithm for denoising of videos corrupted by additive i.i.d. zero mean Gaussian noise with a fixed and known standard deviation. Our algorithm is patch-based. Given a patch from a frame in the video, the algorithm collects similar patches from the same and adjacent frames. All the patches in this group are denoised using a transform-based approach that involves hard thresholding...

Tensor Approximation Algorithm based on High Order Singular Value Decomposition

To solve some problems which JPEG compression obtains results of poor reconstruction quality and high computational complexity for image containing more high frequency information, a novel tensor approximation algorithm based on high order singular value decomposition has been proposed. The new algorithm respects each image both gray image and color image as a high order tensor. It transforms t...

Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)

Tensor decompositions have rich applications in statistics and machine learning, and developing efficient, accurate algorithms for the problem has received much attention recently. Here, we present a new method built on Kruskal’s uniqueness theorem to decompose symmetric, nearly orthogonally decomposable tensors. Unlike the classical higher-order singular value decomposition which unfolds a ten...

Appendix: Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)