Results 251 to 260 of about 1,077,071 (292)

Tucker Decomposition and Log-Gabor Feature-Based Quality Assessment for the Screen Content Videos

IEEE Transactions on Instrumentation and Measurement
With the rapid development of mobile communication and digital multimedia technology, the evaluation metric dedicated to visual information processing toward the screen content is in urgent need. In this article, a full-reference video quality assessment
Hailiang Huang, Huanqiang Zeng, Jing Chen, Junhui Hou, Chih-Hsien Hsia, Kai-Kuang Ma   +5 more
semanticscholar   +1 more source

Black-Box Approximation and Optimization with Hierarchical Tucker Decomposition

arXiv.org
We develop a new method HTBB for the multidimensional black-box approximation and gradient-free optimization, which is based on the low-rank hierarchical Tucker decomposition with the use of the MaxVol indices selection procedure.
G. Ryzhakov, A. Chertkov, Artem Basharin, Ivan V. Oseledets   +3 more
semanticscholar   +1 more source

Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition

Radiology: Artificial Intelligence
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version.
Tobias Weber, Jakob Dexl, David Rügamer, Michael Ingrisch   +3 more
semanticscholar   +1 more source

A novel data-driven 3D site characterisation method: Tucker decomposition-Bayesian compressive sensing and benchmarking study

Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
Site characterisation plays a pivotal role in geotechnical design and analysis. With advancements in machine learning and other digital technologies, data-driven site characterisation (DDSC) has garnered substantial interest in data-centric geotechnics ...
Menglu Huang, T. Shuku, T. Shibata, Shin-ichi Nishimura   +3 more
semanticscholar   +1 more source

Enhanced Low-Rank and Sparse Tucker Decomposition For Image Completion

IEEE International Conference on Acoustics, Speech, and Signal Processing
Recent advancements in low-rank tensor measures have addressed tensor completion challenges, particularly in image completion (IC) tasks. However, the most current low rankness is often based on the unfolding matrix’s rank summation. Moreover, it ignores
W. Gong, Zhejun Huang, Lili Yan
semanticscholar   +1 more source

On optimizing distributed non-negative Tucker decomposition

Proceedings of the ACM International Conference on Supercomputing, 2019
The 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, Shivmaran S. Pandian, Saurabh Raje, Yogish Sabharwal   +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), 2019
With 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, Wenqi Sun, Junfeng Zhao, Yasha Wang, Bing Xie   +4 more
openaire   +1 more source

On Fast algorithms for orthogonal Tucker decomposition

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
We 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, Andrzej Cichocki, Petr Tichavsky   +2 more
openaire   +1 more source

Local Learning Rules for Nonnegative Tucker Decomposition

2009
Analysis of data with high dimensionality in modern applications, such as spectral analysis, neuroscience, chemometrices naturally requires tensorial approaches different from standard matrix factorizations (PCA, ICA, NMF). The Tucker decomposition and its constrained versions with sparsity and/or nonnegativity constraints allow for the extraction of ...
Anh Huy Phan, Andrzej Cichocki
openaire   +1 more source

Non-negative Tucker decomposition with graph regularization and smooth constraint for clustering

Pattern Recognition, 2023
Qilong Liu, Linzhang Lu, Zhen Chen
semanticscholar   +1 more source