Results 221 to 230 of about 3,384 (256)

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

MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction. [PDF]

Brief Bioinform
Tian H, Yang K, Liu Z, Gao H, Yu J, Zhang L, Zhou X.   +6 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.
Junjun Pan, Michael K Ng, Xiongjun Zhang   +2 more
exaly   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

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
exaly   +2 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

Algorithms for Sparse Nonnegative Tucker Decompositions

Neural Computation, 2008
There is a increasing interest in analysis of large-scale multiway data. The concept of multiway data refers to arrays of data with more than two dimensions, that is, taking the form of tensors. To analyze such data, decomposition techniques are widely used.
Morten Mørup, Lars Kai Hansen, Sidse Marie Arnfred   +2 more
openaire   +3 more sources

D-Tucker: Fast and Memory-Efficient Tucker Decomposition for Dense Tensors

2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020
Given a dense tensor, how can we find latent patterns and relations efficientlyƒ Existing Tucker decomposition methods based on Alternating Least Square (ALS) have limitations in terms of time and space since they directly handle large dense tensors to obtain the result of Tucker decomposition.
Jun-Gi Jang, U Kang
openaire   +1 more source