‎‎The tensor product is the fundemental ingredient for extending one-dimensional techniques of filtering and compression in signal preprocessing to higher dimensions‎. ‎Woven frames play ‎ a crucial role in signal preprocessing and distributed data processing‎. Motivated by these facts, we have investigated the tensor product of woven frames and presented some of their properties. Besides...

Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. The typical linear subspace learning algorithms include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projection (LPP). All of these methods consider an n1 × n2 ...

A nonparametric factor analysis framework is developed for tensor-variate data. The dictionary elements (factor loadings) are inferred as tensors, as opposed to vectors. Our tensor-factor analysis (TFA) framework is developed in the context of the beta-process factor analysis (BPFA) using a nonparametric tensor decomposition for each dictionary element. We extend the multiplicative gamma prior ...

the ordinary tensor product of modules is defined using bilinear maps (bimorphisms), that are linear in eachcomponent. keeping this in mind, linton and banaschewski with nelson defined and studied the tensor product in an equational category and in a general (concrete) category k, respectively, using bimorphisms, that is, defined via the hom-functor on k. also, the so-called sesquilinear, or on...

‎the object of the present paper is to study spacetimes admitting‎ ‎quasi-conformal curvature tensor‎. ‎at first we prove that a quasi-conformally flat spacetime is einstein‎ ‎and hence it is of constant curvature and the energy momentum tensor of such a spacetime satisfying‎ ‎einstein's field equation with cosmological constant is covariant constant‎. ‎next‎, ‎we prove that if the perfect...

Tensor analysis is a powerful tool for multiway problems in data mining, signal processing, pattern recognition and many other areas. Nowadays, the most important challenges in tensor analysis are efficiency and adaptability. Still, the majority of techniques are not scalable or not applicable in streaming settings. One of the promising frameworks that simultaneously addresses these two issues ...

In this paper instability of planar anisotropic sheet metal during a few forming processes is investigated for the first time. For this reason components of the constitutive tangent tensor for planar anisotropic sheets are developed. By using the above tensor location of necking is predicted. Direction of the shear band is also predicted using the acoustic tensor. A finite element program is pr...