Results 231 to 240 of about 1,077,071 (292)

Distributed Nonlocal Coupled Hierarchical Tucker Decomposition for Hyperspectral Image Fusion

IEEE Geoscience and Remote Sensing Letters, 2023
Hyperspectral image (HIS) super-resolution aims to fuse a low-spatial-resolution HSI (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) to obtain a high-resolution HSI (HR-HSI).
Peng Zheng, J. Sun, Yang Xu, Yi Zhang, Zhihui Wei, J. Plaza, A. Plaza, Zebin Wu   +7 more
semanticscholar   +1 more source

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

Spatio-Temporal Traffic Data Recovery Via Latent Factorization of Tensors Based on Tucker Decomposition

IEEE International Conference on Systems, Man and Cybernetics, 2023
Complete and valid spatio-temporal traffic data play a vital role in intelligent transportation systems applications, such as congestion avoidance and route guidance.
Jia-Wei Mi, Hao Wu, Weiling Li, Xin Luo
semanticscholar   +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.
Mørup, Morten, Hansen, Lars Kai, Arnfred, Sidse M.   +2 more
openaire   +3 more sources

Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data

International Conference on Learning Representations, 2023
Tucker decomposition is a powerful tensor model to handle multi-aspect data. It demonstrates the low-rank property by decomposing the grid-structured data as interactions between a core tensor and a set of object representations (factors).
Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, R. Kirby, Shandian Zhe   +5 more
semanticscholar   +1 more source

Efficient algorithms for Tucker decomposition via approximate matrix multiplication

Advances in Computational Mathematics, 2023
This paper develops fast and efficient algorithms for computing Tucker decomposition with a given multilinear rank. By combining random projection and the power scheme, we propose two efficient randomized versions for the truncated high-order singular ...
Maolin Che, Yimin Wei, Hong Yan
semanticscholar   +1 more source

Robust Feature Extraction via ℓ∞-Norm Based Nonnegative Tucker Decomposition

IEEE transactions on circuits and systems for video technology (Print), 2023
Feature extraction plays an indispensable role in image and video technology. However, it is difficult for traditional matrix based feature extraction methods to handle massive multi-dimensional data.
Bilian Chen, Jiewen Guan, Zhening Li, Zhehao Zhou   +3 more
semanticscholar   +1 more source

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

TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation

The Web Conference
In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on large-scale datasets, but this also results in significant training costs ...
Jiaqing Zhang, Mingjia Yin, Hao Wang, Yawen Li, Yuyang Ye, Xingyu Lou, Junping Du, Enhong Chen   +7 more
semanticscholar   +1 more source