Results 221 to 230 of about 3,449 (252)
Tucker decomposition-based temporal knowledge graph completion [PDF]
Knowledge-Based Systems, 2022 Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years witness that many algorithms for link prediction and knowledge graphs embedding have been designed to ...
Jianhua Tao
exaly +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Algorithms for Sparse Nonnegative Tucker Decompositions
Neural Computation, 2008There 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 +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), 2020Given 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
On Fast algorithms for orthogonal Tucker decomposition
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014We propose algorithms for Tucker tensor decomposition, which can avoid computing singular value decomposition or eigenvalue decomposition of large matrices as in the work-horse higher order orthogonal iteration (HOOI) algorithm. The novel algorithms require computational cost of O(I 3 R), which is cheaper than O(I 3 R + IR 4 + R 6 ) of HOOI for ...
Anh Huy Phan 0001 +2 more
openaire +1 more source
Nonnegative Tucker decomposition with alpha-divergence
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008Nonnegative tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnega- tivity constraints are incorporated into Tucker model. In this paper we consider alpha-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD.
Yong-Deok Kim +2 more
openaire +1 more source
Parallel Tucker Decomposition with Numerically Accurate SVD
50th International Conference on Parallel Processing, 2021Tucker decomposition is a low-rank tensor approximation that generalizes a truncated matrix singular value decomposition (SVD). Existing parallel software has shown that Tucker decomposition is particularly effective at compressing terabyte-sized multidimensional scientific simulation datasets, computing reduced representations that satisfy a specified
Zitong Li, Qiming Fang, Grey Ballard
openaire +1 more source
An Efficient Randomized Algorithm for Computing the Approximate Tucker Decomposition
Journal of Scientific Computing, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maolin Che +2 more
openaire +1 more source
Double‐Tucker Decomposition and Its Computations
Numerical Linear Algebra with ApplicationsABSTRACTThe famous Tucker decomposition has been widely and successfully used in many fields. However, it often suffers from the curse of dimensionality due to the core tensor and large ranks. To tackle this issue, we introduce an additional core tensor into Tucker decomposition and propose the so‐called double‐Tucker (dTucker) decomposition.
Mengyu Wang, Honghua Cui, Hanyu Li
openaire +1 more source
On optimizing distributed non-negative Tucker decomposition
Proceedings of the ACM International Conference on Supercomputing, 2019The Tucker decomposition generalizes singular value decomposition (SVD) to high dimensional tensors. It factorizes a given N-dimensional tensor as the product of a small core tensor and a set of N factor matrices. Non-negative Tucker Decomposition (NTD) is a variant that imposes the constraint that the entries of the core and the factor matrices must ...
Venkatesan T. Chakaravarthy +3 more
openaire +1 more source
Supervised Nonnegative Tucker Decomposition for Computational Phenotyping
2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019With the availability of Electronic Health Records (EHR) data, lots of predictive tasks in medical practice seem solvable by building predictive models. However, EHR data always contains various medical concepts (e.g., diagnosis, medicines, lab tests) with high dimensions and mass correlations among them.
Kai Yang +4 more
openaire +1 more source