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L1-Norm Tucker Tensor Decomposition [PDF]
IEEE Access, 2019 Tucker decomposition is a standard multi-way generalization of Principal-Component Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker decomposition has been shown to be sensitive against faulty data, due to its L2-norm-based ...
Dimitris G. Chachlakis +2 more
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Rank-Adaptive Tensor Completion Based on Tucker Decomposition [PDF]
Entropy, 2023 Tensor completion is a fundamental tool to estimate unknown information from observed data, which is widely used in many areas, including image and video recovery, traffic data completion and the multi-input multi-output problems in information theory ...
Siqi Liu, Xiaoyu Shi, Qifeng Liao
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Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution [PDF]
Frontiers in Neuroinformatics, 2022 Limited by hardware conditions, imaging devices, transmission efficiency, and other factors, high-resolution (HR) images cannot be obtained directly in clinical settings.
Huidi Jia +11 more
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Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition [PDF]
SensorsModular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative ...
Xiaoling Chen +5 more
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Efficient enhancement of low-rank tensor completion via thin QR decomposition [PDF]
Frontiers in Big DataLow-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues.
Yan Wu, Yunzhi Jin
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Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
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Design and Implementation of Tucker Decomposition Module Based on CUDA and CUBLAS [PDF]
Jisuanji gongcheng, 2019 Because tensor Tucker decomposition is widely used in image processing,face recognition,signal processing and other fields,Tucker decomposition algorithm becomes a key research object.However,the current popular Tucker decomposition algorithm needs to ...
ZHOU Qi,CHAI Xiaoli,MA Kejie,YU Zeren
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Multimodal Tucker Decomposition for Gated RBM Inference
Applied Sciences, 2021 Gated networks are networks that contain gating connections in which the output of at least two neurons are multiplied. The basic idea of a gated restricted Boltzmann machine (RBM) model is to use the binary hidden units to learn the conditional ...
Mauricio Maldonado-Chan +2 more
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Tensor decomposition based networks for nuclei segmentation and classification
Electronics Letters, 2022 Nuclei segmentation and classification for Haematoxylin & Eosin stained histology images is a challenging task because of many issues, large intra‐class variability among nuclei, overlapping nuclei etc.
Jinhao Chen, Zhao Chen
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Discriminative Nonnegative Tucker Decomposition for Tensor Data Representation
Mathematics, 2022 Nonnegative Tucker decomposition (NTD) is an unsupervised method and has been extended in many applied fields. However, NTD does not make use of the label information of sample data, even though such label information is available.
Wenjing Jing, Linzhang Lu, Qilong Liu
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