Results 211 to 220 of about 3,449 (252)

Tensor Methods in Biomedical Image Analysis. [PDF]

J Med Signals Sens
Sedighin F.
europepmc   +1 more source

Minimally invasive capsule-string device enables spatially resolved microbiome profiling across the upper gastrointestinal tract. [PDF]

Gut Microbes
Garvey K, Harris JK, Furuta GT, Occhipinti KA, Wagner BD, Fernandez J, VeDepo M, Fouquier J, Hill EB, Robertson CE, Ackerman S, Shandas R.   +11 more
europepmc   +1 more source

KRONECKER PRODUCT OF TENSORS AND HYPERGRAPHS: STRUCTURE AND DYNAMICS. [PDF]

SIAM J Matrix Anal Appl
Pickard J, Chen C, Stansbury C, Surana A, Bloch A, Rajapakse I.   +5 more
europepmc   +1 more source

Orthogonal Nonnegative Tucker Decomposition [PDF]

SIAM Journal of Scientific Computing, 2021
In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given.
Michael Ng, Xiongjun Zhang
exaly   +3 more sources

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Tucker decomposition and applications

Materials Today: Proceedings, 2021
Abstract Non-negative Tucker decomposition is a well-known higher order tensor decomposition method, where non-negativity is imposed on higher order Tucker decomposition. This paper is associated to the reality behind higher order Tucker decomposition which is computed with the help of HOSVD and its extension HOOI.
Seema Saini, Vineet Bhatt
exaly   +2 more sources

Scalable Symmetric Tucker Tensor Decomposition

SIAM Journal on Matrix Analysis and Applications
We study the best low-rank Tucker decomposition of symmetric tensors. The motivating application is decomposing higher-order multivariate moments. Moment tensors have special structure and are important to various data science problems. We advocate for projected gradient descent (PGD) method and higher-order eigenvalue decomposition (HOEVD ...
, Tamara Kolda
exaly   +4 more sources

Nonnegative Tucker Decomposition

2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper we consider the Tucker model with nonnegativity constraints and develop a new tensor factorization method, referred to as nonnegative ...
Yong-Deok Kim, Seungjin Choi
openaire   +1 more source

Compression of hyperspectral images based on Tucker decomposition and CP decomposition

Journal of the Optical Society of America A, 2022
Hyperspectral imagers are developing towards high resolution, high detection sensitivity, broad spectra, and wide coverage, which means that hyperspectral data are getting more and more substantial. This brings a great challenge to data storage and real-time transmission of hyperspectral data.
Lei, Yang, Jinsong, Zhou, Juanjuan, Jing, Lidong, Wei, Yacan, Li, Xiaoying, He, Lei, Feng, Boyang, Nie   +7 more
openaire   +2 more sources

Static and Streaming Tucker Decomposition for Dense Tensors

ACM Transactions on Knowledge Discovery From Data, 2023
Given a dense tensor, how can we efficiently discover hidden relations and patterns in static and online streaming settings? Tucker decomposition is a fundamental tool to analyze multidimensional arrays in the form of tensors. However, existing Tucker decomposition methods in both static and online streaming settings have limitations of efficiency ...
U Kang, Jun-Gi Jang
exaly   +2 more sources