Tensor decomposition and applications

We develop a technique for multigrid adaptive reduction of data (MGARD). Special attention is given to the case of tensor product grids, where our approach permits the use of nonuniformly spaced grids in each direction, which can prove problematic for many types of data reduction methods. An important feature of our a…

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A new vascular structure segmentation method, which is based on a cylindrical flux-based higher order tensor (HOT), is presented. On a vessel structure, the HOT naturally models branching points, which create challenges for vessel segmentation algorithms. In a general linear HOT model embedded in 3D, one has to work w…

• GÖZDE ÜNAL

Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking serv…

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• HALİM ÖZDEMİR

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• SEMİH ERGİN

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• ÖMER NEZİH GEREK

Summary We propose subspace methods for three‐parameter eigenvalue problems. Such problems arise when separation of variables is applied to separable boundary value problems; a particular example is the Helmholtz equation in ellipsoidal and paraboloidal coordinates. While several subspace methods for two‐parameter eig…

• EMRE MENGİ

This work is an extension of very recently developed decomposition method for matrices. That method has been called "Tridiagonal Matrix Enhanced Multivariance Product Representation, or briefly, TMEMPR. Here, in this work our ultimate goal has been taken as the decomposition of a univariate linear integral operator. I…

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