Results 221 to 230 of about 690,102 (265)

Low-Rank Tensor Decomposition for Cross-Bispectral Analysis of EEG Data

Kaziki D, Engel AK, Nolte G.
europepmc   +1 more source

Family SES amplifies genetic influences on organized activity involvement from childhood to young adulthood: evidence from the German twin family panel. [PDF]

Front Sociol
Liu Y.
europepmc   +1 more source

Relation-aware context aggregation framework for sparse knowledge graph completion. [PDF]

Sci Rep
Guan S, Han X.
europepmc   +1 more source

GNN-FTuckER: A novel link prediction model for identifying suitable populations for tea varieties. [PDF]

PLoS One
Li J, Yang B, Liu J, Wang X, Wu Z, Huang Q, He P.   +6 more
europepmc   +1 more source

Exploring the role of binge eating in the association between ADHD and BMI: A twin study

You Y, McAdams TA, Oginni O, Liu C, Herle M, Zavos HM.   +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   +4 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
exaly   +2 more sources

Parallel Randomized Tucker Decomposition Algorithms

SIAM Journal of Scientific Computing
The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. We propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization. We present two algorithms that scale to large data and many processors, significantly reduce both computation and communication ...
Grey Ballard, Zitong Li
exaly   +5 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   +3 more sources