Many idealized problems in signal processing, machine learning and statistics can be reduced to the problem of finding the symmetric canonical decomposition of an underlying symmetric and orthogonally decomposable (SOD) tensor. Drawing inspiration from the matrix case, the successive rank-one approximations (SROA) scheme has been proposed and shown to yield this tensor decomposition exactly, an...

We investigate optimal conditions for inducing low-rankness of higher order tensors by using convex tensor norms with reshaped tensors. propose the nuclear norm as a generalized approach to reshape be regularized norm. Furthermore, we latent combine multiple analyze generalization bounds completion models proposed and show that novel reshaping lead lower Rademacher complexities. Through simulat...

This paper proposes a novel formulation of the tensor completion problem to impute missing entries of data represented by tensors. The formulation is introduced in terms of tensor train (TT) rank which can effectively capture global information of tensors thanks to its construction by a wellbalanced matricization scheme. Two algorithms are proposed to solve the corresponding tensor completion p...

Multi-way tensor datasets emerge naturally in a variety of domains, such as recommendation systems, bioinformatics, and retail data analysis. The data in these domains usually contains a large number of missing entries. Therefore, many applications in those domains aim at missing value prediction, which boils down to a tensor completion problem. While tensor factorization algorithms can be a po...

We show that the splitting feature of the Einstein tensor, as the first term of the Lovelock tensor, into two parts (the Ricci tensor and the term proportional to the curvature scalar) with the trace relation between them is a common feature of any other homogeneous terms in the Lovelock tensor. Motivated by the principle of general invariance, we find that this property can be generalized, wit...

Compressed sensing extends from the recovery of sparse vectors from undersampled measurements via efficient algorithms to the recovery of matrices of low rank from incomplete information. Here we consider a further extension to the reconstruction of tensors of low multi-linear rank in recently introduced hierarchical tensor formats from a small number of measurements. Hierarchical tensors are a...

The linear transform-based tensor nuclear norm (TNN) methods have recently obtained promising results for completion. main idea of these is exploiting the low-rank structure frontal slices targeted under transform along third mode. However, low-rankness not significant transforms family. To better pursue approximation, we propose a nonlinear TNN (NTTNN). More concretely, proposed composite cons...

we classify the paracontact riemannian manifolds that their rieman-nian curvature satisfies in the certain condition and we show that thisclassification is hold for the special cases semi-symmetric and locally sym-metric spaces. finally we study paracontact riemannian manifolds satis-fying r(x, ξ).s = 0, where s is the ricci tensor.

we obtain the expression of ricci tensor for a $gcr$-lightlikesubmanifold of indefinite complex space form and discuss itsproperties on a totally geodesic $gcr$-lightlike submanifold of anindefinite complex space form. moreover, we have proved that everyproper totally umbilical $gcr$-lightlike submanifold of anindefinite kaehler manifold is a totally geodesic $gcr$-lightlikesubmanifold.